| WUSS |
2026 |
A Structured Agent-Based Framework for Early-Stage Data Science Pipelines |
Dheerusha Tiwari,
Siddharth Birajdar |
source |
14 |
| WUSS |
2026 |
Demystifying Regression Analysis Techniques with R |
Zheyuan Yu,
Kirk Paul Lafler,
Zichun Gao,
Jiaxin Xu,
Zeqi Li,
Ruochen Shao |
|
35 |
| WUSS |
2026 |
Missing Data in PROC MCMC [presentation] |
Danny R. Modlin |
source |
7 |
| WUSS |
2026 |
SQL Everywhere! Reuse your PROC SQL Code in R and Python with DuckDB |
Matthew T. Slaughter,
Isaiah Lankham,
Kirk Paul Lafler |
source |
19 |
| WUSS |
2026 |
The Essentials of SAS Dates and Times |
Derek P. Morgan |
source |
24 |
| PharmaSUG CN |
2026 |
Efficient Strategies for Handling Data Issues in Clinical Trial Submissions |
Kate Sun |
source |
7 |
| PharmaSUG CN |
2026 |
From SAP to TFL: An AI-Assisted Workflow Framework for Automated Shells and TFLs |
Xiaoyu Ding,
Guoqing Zhao,
Huafeng Gao,
Zhiyuan Guo |
source |
18 |
| PharmaSUG CN |
2026 |
Handling Missing Data in Analgesia Clinical Trials and Corresponding SAS Implementation |
Jingbo Zhu,
Jiehan Zhang |
source |
17 |
| PharmaSUG |
2026 |
A Statistical Programmer-s Guide to Tipping Point Analysis in SAS |
Ang Xu |
source |
9 |
| PharmaSUG |
2026 |
Applications of PROC COMPARE to Parallel Programming and other projects |
Jayanth Iyengar |
source |
21 |
| PharmaSUG |
2026 |
Boolean Rhapsody: 50 shades of true Is this the real code? Is this just fantasy? |
Charu Shankar |
source |
36 |
| PharmaSUG |
2026 |
Building ADaM Datasets from Scratch using R: A SAS programmer-s Perspective |
Patel Mukesh,
Nilesh Patel |
source |
15 |
| PharmaSUG |
2026 |
DOSEON: Fuzzy Matching DOSE Date Intervals ON Analysis Dates Across SAS (PROC SQL, SAS Macro, DATA Step, and PROC FCMP) |
Inka Leprince,
Troy Martin Hughes |
source |
39 |
| PharmaSUG |
2026 |
Efficiency Techniques in SAS 9.4 |
Stephen B. Sloan |
source |
9 |
| PharmaSUG |
2026 |
From Chaos to Clarity: A Programmer-s Perspective on Standardizing Population Pharmacokinetic (PopPK) Data for Regulatory Success |
Naveen Muppalla,
Shibani Harite |
source |
7 |
| PharmaSUG |
2026 |
From Manual to Automated": An SAS and R-Based Toolkit for Scalable SDTM Generation |
Hardik Sheth,
Eldho Alias |
source |
8 |
| PharmaSUG |
2026 |
Goodbye SAS, Hello R: Practical Workflows for CDISC Standards |
Madeleine Penniston,
Alyssa M. Wittle |
source |
12 |
| PharmaSUG |
2026 |
Implementing Dynamic Time Warping in SAS 9.4 Using PROC IML: An Alternative Approach for Time-Series Model Evaluation |
Yida Bao,
Philippe Gaillard,
Wei Yao,
Zheng Zhang,
Rui Wang |
source |
13 |
| PharmaSUG |
2026 |
Improving AI SAS-to-R Code Migration via an Intermediate Design Document Layer |
Junze Zhang,
Amy Zhang |
source |
7 |
| PharmaSUG |
2026 |
Ready for Next Level SDTMs and ADaMs Compliance with End-to-End Processing? |
Sunil Kumar Gupta,
Tomás Sabat Stofsel |
source |
6 |
| PharmaSUG |
2026 |
Regression Analysis Made Easy Using R |
Zheyuan Yu,
Kirk Paul Lafler,
Zichun Gao,
Jiaxin Xu,
Zeqi Li,
Ruochen Shao |
source |
32 |
| PharmaSUG |
2026 |
SAS Performance Tuning Techniques (From Slow to Scalable) |
Kirk Paul Lafler |
source |
18 |
| PharmaSUG |
2026 |
SAS Programming techniques for efficiency and code optimization |
Jayanth Iyengar |
source |
27 |
| PharmaSUG |
2026 |
Transitioning from SAS to R: Implementing Reproducible R Workflows for TLF Validation |
Poornima Alavandi |
source |
17 |
| PHUSE US |
2026 |
Advancing Precision Medicine and Regulatory Readiness: Data Harmonisation and Statistical Methods for Companion Diagnostics Bridging Studies |
Ummul Vara Qurratul Ain |
source
presentation source |
10 |
| PHUSE US |
2026 |
An AI-Driven Framework for Automated Statistical Analysis Plan Generation in Clinical Trials |
Qingyuan Zhang |
source
presentation source |
8 |
| PHUSE US |
2026 |
Bridging Analysis and Strategy: The Transformative Role of Statistical Analysts in Drug Development |
Lingsong Yun |
source
presentation source |
17 |
| PHUSE US |
2026 |
Common Issues in BIMO Clinical Site Dataset Packages |
Michael Beers |
source
presentation source |
7 |
| PHUSE US |
2026 |
Data Can't Do It Alone: The Untold Power of Soft Skills in IDMCs |
Maira Pinto |
source
presentation source |
9 |
| PHUSE US |
2026 |
Data Designer: Empowering Data Preparation, Transformation, ARD Creation and Summarization for Insights and Decision Making |
Mukul K Mittal,
Sanjeev Rathore |
source
presentation source |
8 |
| PHUSE US |
2026 |
Deriving Phantom Records in BDS Datasets To Support the FDA's Standard Safety Figures for Missing Data Analysis |
Guowei Wu,
Ruchitbhai Patel |
source
presentation source |
6 |
| PHUSE US |
2026 |
Development Process of the Bioresearch Monitoring (BIMO) Package and the Obstacles Successfully Addressed [presentation] |
Sarad Nepal,
Murali Neela |
source |
19 |
| PHUSE US |
2026 |
End-to-End Workflow for CDISC Estimand Framework in Clinical Trials |
Hiral Raval,
Simon Luo |
source
poster source |
12 |
| PHUSE US |
2026 |
Ensuring Reliability of Medical Devices Through Statistical Oversight in the Age of AI |
Parvee Kumar,
Shrutimita Pokhariyal |
source
presentation source |
11 |
| PHUSE US |
2026 |
Estimand-Aligned Multiple Imputation Strategies in CNS Trials with Complex Intercurrent Events |
Veera Thota,
Ian Lees |
source
presentation source |
6 |
| PHUSE US |
2026 |
Framework for Incorporating Estimands within the Statistical Analysis Plan |
Inderjeet Arora |
source
presentation source |
9 |
| PHUSE US |
2026 |
Fuzzy Matching DOSE Date Intervals ON Analysis Dates Across SAS (DATA Step, PROC SQL, SAS Macro, and PROC FCMP) |
Inka Leprince,
Troy Martin Hughes |
source
presentation source |
32 |
| PHUSE US |
2026 |
Is Complexity Worth It? A Case Study Comparing Multiple Imputation Strategies |
Elnaz Ghadimi,
David Bushnell |
source
poster source |
4 |
| PHUSE US |
2026 |
Leveraging Python for Synthetic External Vendor Data Generation for eCOA and ePRO |
Hitesh Raval |
source
presentation source |
9 |
| PHUSE US |
2026 |
Ready for Next-Level SDTM and ADaM Compliance with End-to-End Processing? |
Sunil Kumar Gupta |
source
presentation source |
4 |
| PHUSE US |
2026 |
Submitting RWD: Where We Are and Where We Are Going |
Ingeborg Holt,
Sarah Ferko,
Jeffrey M. Abolafia |
source
presentation source |
24 |
| PHUSE US |
2026 |
You Can't Just Cut the Gordian Knot: Approaches to Real-World Data Quality |
S. Robert Collins |
source
presentation source |
4 |
| PHUSE APAC |
2026 |
Advancing Clinical Trial Analysis with Trimmed Means: Estimands, Intercurrent Events, and Regulatory Alignment |
Prasanth Palanisamy |
source
presentation source |
9 |
| PHUSE APAC |
2026 |
aRe You Ready!! Fun, Fast & Functional Coding Tips |
Xeviers Koner |
source
presentation source |
4 |
| PHUSE APAC |
2026 |
Cytel FSP - AI to Enable the Biometrics Workforce of the Future [presentation] |
Corey Dunham |
source |
11 |
| PHUSE APAC |
2026 |
External Comparator Studies in the Era of Real-World Data |
Vishal Goriya,
Anshul Sinha,
Gerd Rippin |
source
presentation source |
5 |
| PHUSE APAC |
2026 |
From Missing to Meaningful: Validating Multiple Imputation Techniques for Reliable Analysis |
Karthick Jayaraman |
source
presentation source |
8 |
| PHUSE APAC |
2026 |
Innovative Strategies for Managing Missing Data in Real-World Data: Insights from APAC Global Capability Centers |
Likhita Kolli,
Tuhin James Paul |
source
poster source |
12 |
| PHUSE APAC |
2026 |
Invisible Influences: Estimands, Assumptions and Sensitivity in Inflammation & Immunologic Trial Interpretation |
Sukanya Erramachetty Subramanyam |
source
presentation source |
8 |
| PHUSE APAC |
2026 |
Leveraging Large Language Models (LLMs) for Missing Data Imputation and Interpretation of Real-World Evidence Outputs |
Spencer Langerman,
Rahul Manohar Somavanshi |
source
presentation source |
12 |
| PHUSE APAC |
2026 |
Precision Timing - Implementing Epoch Calculations with R for Clinical Data Integrity |
Sheik Ibrahim,
Ram Mohan Konda |
source
presentation source |
6 |
| PHUSE APAC |
2026 |
Reimagining Clinical Data Exploration: Animated Data Visualizations for Interactive Clinical Trial Storytelling |
Tony Mathew |
source
presentation source |
6 |
| PHUSE APAC |
2026 |
SAS to R: CHEAT SHEET - A Practical Guide for Clinical Programmers |
Lekitha JK,
Jalendhar Ettedi |
source
poster source |
24 |
| PHUSE APAC |
2026 |
Summary Outputs Made Simple: A Mirror View for SAS Programmers Exploring R |
Venkata Koteswara Rao Rayala |
source
poster source |
7 |
| PHUSE APAC |
2026 |
Tipping Point Analysis in R and SAS: A Comparative Approach to Handling Missing Data in Clinical Research |
Sushil Kale,
Jeet Agrawal |
source
poster source |
7 |
| PHUSE APAC |
2026 |
Want to be SASsy? |
Parthasarathi Kongara |
source
presentation source |
8 |
| PHUSE EU |
2025 |
Aspirations of language agnosticism - experiences expanding the programming stack with python |
Kaj Arai Hejlesen,
Michael Svingel |
source
presentation source |
9 |
| PHUSE EU |
2025 |
Bayesian Inference for Medical Device Reliability: Improving Safety and Effectiveness Assessments |
Shrutimita Pokhariyal |
source
presentation source |
7 |
| PHUSE EU |
2025 |
Contributions to CAMIS PHUSE Project in 2025: Tobit Regression, Reference-based Multiple Imputation and Tipping Point Analysis |
Yannick Vandendijck |
source
presentation source |
12 |
| PHUSE EU |
2025 |
Hybrid Way To Develop Patient Narratives [presentation] |
Loskutov Volodymyr,
Oleh Buno |
source |
16 |
| PHUSE EU |
2025 |
JSON for Submissions: Why You Should Care, What You Need To Know, and What To Do Now |
Bill Qubeck,
Sridhar Vijendra |
source
presentation source |
13 |
| PHUSE EU |
2025 |
Keeping it Real: A Tiered Approach to Real-World Data Quality by Leveraging Synthetic Data |
Lisa Murch,
William Kuan |
source
presentation source |
10 |
| PHUSE EU |
2025 |
Mind the Gap: Imputing Missing Dosing Times in QD & BID Regimens Using ADPPK Guidelines |
Aschwin van der Giesen,
Gerly van der Vleuten |
source
presentation source |
8 |
| PHUSE EU |
2025 |
Multiple Imputations Overview of Missing Data Problem, Solution Methods and SAS Implementation |
Dmytro Kalinin |
source
presentation source |
13 |
| PHUSE EU |
2025 |
New Multiple Imputation Approaches with SAS |
Andrea Parodi |
source
presentation source |
2 |
| PHUSE EU |
2025 |
Our Journey in Integrating External Control Arms (ECAs) and Real-World Data (RWD) for Rare Disease Trials |
Cyril Sombrin,
Gautham Selvaraj |
source
poster source |
4 |
| PHUSE EU |
2025 |
Pharmacokinetics Is Simple... Sometimes |
Vincent Buchheit |
source
presentation source |
12 |
| PHUSE EU |
2025 |
Poster PP11 |
Pragmatism Meets Precision,
Andrea Parodi |
source
poster source |
4 |
| PHUSE EU |
2025 |
Statistical Endpoints and Analysis in Diagnostic Imaging: The Matrix Has You and Your Data |
Srinivas Lanka,
James Sykes |
source
presentation source |
17 |
| PHUSE EU |
2025 |
The Value of Clinical Trial Data: From Submission to Innovation |
Anne Katrine Alsing |
source
presentation source |
8 |
| MWSUG |
2025 |
Applications of PROC COMPARE to Parallel Programming and other projects |
Jayanth Iyengar |
source |
21 |
| MWSUG |
2025 |
Fifteen Functions to Supercharge Your SAS Code |
Joshua M. Horstman |
source |
8 |
| MWSUG |
2025 |
Last Observation Carried Forward (LOCF) in Longitudinal Clinical Studies: Adopting a Functional Approach to Imputing Missing Values Using PROC FCMP, the SAS Function Compiler |
Troy Martin Hughes |
source |
9 |
| MWSUG |
2025 |
Make You Holla' Tikka Masala: Creating User-Defined Informats Using the PROC FORMAT OTHER Option To Call User-Defined FCMP Functions That |
Troy Martin Hughes |
source |
10 |
| MWSUG |
2025 |
Mind the Gaps: Automating Multiple Imputation in Clinical Trial Workflows [presentation] |
Crisa Chen,
Jiangang Cai |
source |
30 |
| SESUG |
2025 |
Data Diagnostics with Base SAS |
Mark L. Jordan |
|
38 |
| SESUG |
2025 |
Efficiency Techniques in SAS 9 |
Stephen B. Sloan |
|
9 |
| SESUG |
2025 |
Last Observation Carried Forward (LOCF) in Longitudinal Pharmaceutical and Clinical Trials Studies: Adopting a Functional Approach to Imputing Missing Values Using PROC FCMP, the SAS Function Compiler |
Troy Martin Hughes |
|
9 |
| SESUG |
2025 |
Lookup Tables (LUT) in SAS and Data-Driven Programming |
Imelda C. Go,
Abbas S. Tavakoli |
|
17 |
| SESUG |
2025 |
Spring Cleaning for CSVs: Prepare for PROC IMPORT |
Brooke Ellen Delgoffe |
|
16 |
| WUSS |
2025 |
Applications of PROC COMPARE to Parallel Programming and other projects |
Jayanth Iyengar |
source |
21 |
| WUSS |
2025 |
Last Observation Carried Forward (LOCF) in Longitudinal Clinical Studies: Adopting a Functional Approach to Imputing Missing Values Using PROC FCMP, the SAS Function Compiler |
Troy Martin Hughes |
source |
9 |
| WUSS |
2025 |
Rerun or Reboot? Reproducibility in a Golden Age of Generative AI |
Wendy Christensen |
source |
5 |
| PharmaSUG |
2025 |
A Customizable Framework in R for Presenting BICR Data in a User-Friendly Format |
Reneta Hermiz,
Jing Ji |
source |
14 |
| PharmaSUG |
2025 |
An End-to-End Workflow for TFL Generation using R: MMRM Applications and Comparative Insights with SAS |
Kai Lei,
Jiaqiang Zhu,
Margaret Huang |
source |
14 |
| PharmaSUG |
2025 |
Approaches to Developing Multiple Imputation ADaM Datasets |
Kang Xie |
source |
8 |
| PharmaSUG |
2025 |
Common Issues in BIMO Clinical Site Dataset Packages |
Michael Beers |
source |
9 |
| PharmaSUG |
2025 |
Does SDTM Validation Really Require Double Programming? |
Sunil Kumar Gupta,
Tomás Sabat Stofsel |
source |
5 |
| PharmaSUG |
2025 |
Efficiency Techniques in SAS 9 |
Stephen B. Sloan |
source |
9 |
| PharmaSUG |
2025 |
Efficient Strategies for Handling Data Issues in Clinical Trial Submissions |
Kate Sun |
source |
7 |
| PharmaSUG |
2025 |
Ensuring Data Integrity: Techniques for Validating SDTM Datasets in Clinical Research |
Amy Welsh |
source |
8 |
| PharmaSUG |
2025 |
Get Tipsy with Debugging Tips for SAS Code: The After Party |
Lisa A. Mendez,
Richann J. Watson |
source |
16 |
| PharmaSUG |
2025 |
Guidance from the FDA authored "Submitting Patient-Reported Outcome Data in Cancer Clinical Trials" and recommendations for their implementation in study CDISC COA data. |
Charity Quick |
source |
14 |
| PharmaSUG |
2025 |
Handling Health Regulatory Information Requests: Best Practices and Strategies |
Himanshu Patel,
Chintan Pandya |
source |
11 |
| PharmaSUG |
2025 |
Handling Missing Data in External Control Arms: Best Practices, Recommendations, and SAS Code Examples |
Yutong Zhang |
source |
6 |
| PharmaSUG |
2025 |
Last Observation Carried Forward (LOCF) in Longitudinal Patient Studies: A Functional Approach to Imputing Missing Values Using PROC FCMP, the SAS Function Compiler |
Troy Martin Hughes |
source |
9 |
| PharmaSUG |
2025 |
Mastering the Maze of Oncology Endpoints: A Unified SAS Approach for Randomized Controlled Trial Analysis |
Yuxin Wang,
Kelly Chao,
Wenqing Yu,
Hongbing Jin |
source |
11 |
| PharmaSUG |
2025 |
Methodology for AI-driven Outcome Prediction for Patients with Atrial Fibrillation After Transcatheter Aortic Valve Implantation (TAVI) |
Felix Just,
Krishna Padmanabhan,
Parth Jinger,
Amanda Borrow,
Rüdiger Smolnik,
Eva-Maria Fronk,
Neelam Yadav,
Sergei Krivtcov,
Stas Zakharkin,
Martin Unverdorben,
Asmir Vodencarevic |
source |
6 |
| PharmaSUG |
2025 |
Multiple Imputation Techniques in the Context of ADaM Datasets |
Shunbing Zhao,
Linping Li |
source |
11 |
| PharmaSUG |
2025 |
Practical considerations for Intercurrent Events and Multiple Imputation |
David Bushnell |
source |
6 |
| PharmaSUG |
2025 |
Swanky Sankey Enhancements: Transforming a Graph with Pretty Curves to a Research Tool uncovering Deeper Scientific Insights |
Siqi Wang,
Toshio Kimura |
source |
10 |
| PharmaSUG |
2025 |
The Current State of Teaching Biostatistics in Academia: Challenges and Software Solutions |
Lida Gharibvand |
source |
5 |
| PHUSE US |
2025 |
Accelerating PKPD Analysis with Automated DDI Variable Creation: An SAS-Based Approach |
Sunil Mummidisetty,
Harish Ganesan |
source
presentation source |
7 |
| PHUSE US |
2025 |
An Intuitive Way to Derive the Last Known Alive Date in R |
Marckenley Mercie,
Diana Tai |
source
presentation source |
12 |
| PHUSE US |
2025 |
From Roadblocks to Roadmaps: Overcoming Challenges in Registry Data Reporting |
Sivasankar Konda,
Neha Srivastava |
source
presentation source |
4 |
| PHUSE US |
2025 |
Multiple Imputation in Composite Endpoints - It's Not That Hard! |
Ashley Mao |
source
presentation source |
6 |
| PHUSE US |
2025 |
Streamlining Machine Learning Modelling in Neuroscience Clinical Trials Through Cross-Functional Collaboration |
Jing Dai |
source
presentation source |
5 |
| PHUSE US |
2025 |
Transforming Clinical Trials: Leveraging Bayesian Statistics in Innovative Trial Design, Analysis, Applications and Navigating Challenges |
Jaskaran Singh Saini |
source
presentation source |
11 |
| MWSUG |
2024 |
A General Review of Fuzzy Matching Techniques and Their Capabilities Using SAS |
Kirk Paul Lafler |
source |
31 |
| MWSUG |
2024 |
An Animated Guide to Proc Report Internals |
Russell M. Lavery |
source |
31 |
| MWSUG |
2024 |
Best Practices for Efficiency and Code Optimization in SAS programming |
Jayanth Iyengar |
source |
20 |
| MWSUG |
2024 |
Building a SAS Data Viz Toolkit [presentation] |
Sara Richter |
source |
78 |
| MWSUG |
2024 |
Developing Artificial and Convolutional Neural Networks with Python's Keras API for TensorFlow |
Ryan Paul Lafler,
Anna T. K. Wade |
source |
18 |
| MWSUG |
2024 |
Looking for the Missing(ness) Piece |
Louise S. Hadden |
source |
14 |
| MWSUG |
2024 |
Operator, Please? Making the Most of SAS Language Operators [presentation] |
Joshua M. Horstman |
source |
8 |
| MWSUG |
2024 |
Revelation and Exploration for COVID-19 Data: Visual Data Insights and an InfoGeographic Atlas [presentation] |
LeRoy Bessler |
source |
97 |
| PHUSE EU |
2024 |
Artificial Intelligence Potential Use Cases in Clinical Data Engineering |
Deepak Pakalapati,
Mounika Pillamarapu |
source
presentation source |
8 |
| PHUSE EU |
2024 |
Estimands: How to Make ADaM Implementation Your Friend |
Jaclyn Amberg |
source
presentation source |
10 |
| PHUSE EU |
2024 |
Generative AI: A Clinical Programmer's Friend! |
Karl Kennedy,
Daniel Ghali |
source
presentation source |
10 |
| PHUSE EU |
2024 |
Insights of Using {admiralonco} at BMS: Facilitating Oncology Efficacy ADaM Datasets |
Yirong Cao,
Yi Yan |
source
presentation source |
10 |
| PHUSE EU |
2024 |
Mastering Pinnacle 21 for Oncology SDTM |
Daryna Khololovych |
source
presentation source |
7 |
| PHUSE EU |
2024 |
Navigate Clinical Study Report Tables and Listings - The iTable & iListing Project |
Katja Glass |
source
presentation source |
13 |
| PHUSE EU |
2024 |
Py R You SASing Me: Converting SAS Dataset Filters into R and Python |
Matt Metherell |
source
poster source |
5 |
| PHUSE EU |
2024 |
R Evolution: A Guide for Training SAS Programmers |
Michael Walshe |
source
presentation source |
10 |
| PHUSE EU |
2024 |
Strategies to Improve Understanding of Estimands and Analysis Section of Statistical Analysis Plan |
Inderjeet Arora |
source
presentation source |
8 |
| PHUSE EU |
2024 |
Streamlining Complex Analysis: An SAS Macro for Marginal Structural Modelling |
Amine Khemiri |
source
presentation source |
6 |
| PHUSE EU |
2024 |
Unveiling the Tipping Point: An In-depth Analysis |
Varsha Mithun Patil,
Ephicacy Lifescience |
source
presentation source |
8 |
| SESUG |
2024 |
Advanced DATA Step Look-Back and Look-Ahead Techniques |
Joshua M. Horstman |
source |
5 |
| SESUG |
2024 |
Applying Different Data Cutoff Dates for the Analysis of a Study Milestone |
Guowei Zhao,
Hong Qi |
source |
7 |
| SESUG |
2024 |
Automating Manual Processes with SAS: A Practical Example from Higher Education |
Kelly D. Smith,
Elizabeth S. Davis |
source |
5 |
| SESUG |
2024 |
Comparative Tools in SAS: Analyzing Self-Reported versus Imputed Values |
Neesha Nathwani,
Sarah Woodruff |
source |
30 |
| SESUG |
2024 |
Data Diagnostics with Base SAS |
Mark L. Jordan |
source |
37 |
| SESUG |
2024 |
Developing Artificial and Convolutional Neural Networks with Python's Keras API for TensorFlow |
Ryan Paul Lafler,
Anna T. K. Wade |
source |
18 |
| SESUG |
2024 |
Fifteen Functions to Supercharge Your SAS Code |
Joshua M. Horstman |
source |
8 |
| SESUG |
2024 |
Fontcase Macro |
Ranjan Sitaula |
source |
8 |
| SESUG |
2024 |
Log Reviewing Made Easy |
Peter Knapp |
source |
9 |
| SESUG |
2024 |
The SAS Data Set Missing Values Analyzer |
Michael A. Raithel |
source |
20 |
| SESUG |
2024 |
This Works on So Many Levels: Building Summary Tables in SAS with Hash Programming |
David V. Cole |
source |
15 |
| SESUG |
2024 |
Unanticipated Results: Uncovering Five Unexpected Outcomes in PROC SQL Joins |
Kiran Venna |
source |
15 |
| SESUG |
2024 |
Using SAS In Analyzing Trends in Corn Yields, Surface Temperature, and Precipitation in U.S. South Atlantic Census Division States |
Abdullah Khan |
source |
31 |
| WUSS |
2024 |
A Survey of Some of the Most Useful SAS Functions [presentation] |
Ronald P. Cody |
source |
61 |
| WUSS |
2024 |
Advanced DATA Step Look-Back and Look-Ahead Techniques |
Joshua M. Horstman |
source |
5 |
| WUSS |
2024 |
Analyzing Goalkeeper Impact in Premier League Football |
Alec Troy Christiansen,
Benjamin T Houser |
source |
10 |
| WUSS |
2024 |
Developing a SAS Macro for ISNI: Assessing Missing Data Sensitivity Beyond MAR Assumptions |
Bocheng Jing,
L. Grisell Diaz-Ramirez,
W. John Boscardin |
source |
16 |
| WUSS |
2024 |
Developing Artificial and Convolutional Neural Networks with Python's Keras API for TensorFlow |
Ryan Paul Lafler,
Anna T. K. Wade |
source |
18 |
| WUSS |
2024 |
Handling Missing Values in SAS 9 and SAS Viya [presentation] |
Melodie Rush |
source |
102 |
| WUSS |
2024 |
LAST CALL to Get Tipsy with SAS: Tips for Using CALL Subroutines |
Lisa A. Mendez,
Richann J. Watson |
source |
20 |
| WUSS |
2024 |
Multiple Imputation in Interim Analyses [presentation] |
Bill Coar |
source |
29 |
| WUSS |
2024 |
Regression Analysis Made Easy Using SAS Studio |
Zheyuan (Walter) Yu |
source |
42 |
| WUSS |
2024 |
The Essentials of SAS Dates and Times |
Derek P. Morgan |
source |
23 |
| PharmaSUG CN |
2024 |
E9(R1) Program Practice and Its Application in Sensitivity Analysis |
Dawei Jiao |
source |
6 |
| PharmaSUG CN |
2024 |
Implementing Statistical Methods in Companion Diagnostic: A Case Study in NSCLC |
Danxia Hu,
Mandy Gao,
Jianyong Tong |
source |
10 |
| PharmaSUG CN |
2024 |
Implementing the treatment policy estimation for continuous variable with multiple imputation |
Andy Sun |
source |
17 |
| PharmaSUG CN |
2024 |
Programming Perspective: Streamline Estimands, Strategies, and Design ADaM for Estimand |
Yuqin Wu |
source |
10 |
| PharmaSUG |
2024 |
An Introduction to the SAS Transpose Procedure and its Options |
Timothy J. Harrington |
source |
14 |
| PharmaSUG |
2024 |
Bayesian Models with the Power Prior Using PROC BGLIMM |
Yi Gong,
Fang K. Chen |
source |
15 |
| PharmaSUG |
2024 |
Cultivating Success with Non-standard Investigator-sponsored Trial Data for FDA Submissions |
Jai Deep Mittapalli,
Jinit Mistry,
Venkatesulu Salla |
source |
9 |
| PharmaSUG |
2024 |
Dealing with Missing Data: Practical Implementation in SAS and R |
Isabella Y. Wang,
Lauren George,
Jin Xie |
source |
20 |
| PharmaSUG |
2024 |
Implementation of composite estimands for responder analysis based on change from baseline in non-solid tumours |
Reshma Radhakrishnan |
source |
11 |
| PharmaSUG |
2024 |
LAST CALL to Get Tipsy with SAS: Tips for Using CALL Subroutines |
Lisa A. Mendez,
Richann J. Watson |
source |
20 |
| PharmaSUG |
2024 |
Oncology ADaM Datasets Creation Using R Programming: A Comprehensive Approach |
Ishwar Chouhan |
source |
29 |
| PharmaSUG |
2024 |
Protocol Amendments and EDC Updates: Downstream impact on Clinical Trial Data |
Anbu Damodaran,
Ram Gudavalli,
Kumar Bhimavarapu |
source |
11 |
| PharmaSUG |
2024 |
Submitting Patient-Reported Outcome Data in Cancer Clinical Trials - Guidance for Industry Technical Specifications Document [presentation] |
Flora Mulkey |
source |
48 |
| PharmaSUG |
2024 |
The Art of Defensive SAS Programming |
Philip R. Holland |
source |
28 |
| PharmaSUG |
2024 |
The Missing(ness) Piece: Building Comprehensive, Data Driven Missingness Reports and Codebooks Dynamically |
Louise S. Hadden |
source |
15 |
| PharmaSUG |
2024 |
Unraveling the Layers within Neural Networks: Designing Artificial and Convolutional Neural Networks for Classification and Regression Using Python-s Keras & TensorFlow |
Ryan Paul Lafler,
Anna T. K. Wade |
source |
18 |
| PHUSE US |
2024 |
Beyond Outliers: Unsupervised Anomaly Detection in Clinical Trial Data |
Helena Yaben,
Kian Norouzi |
source
presentation source |
9 |
| PHUSE US |
2024 |
EOSS_MMRM V1.0: A Shiny Application for Estimand-Oriented Sample Size and Power Simulation Using MMRM Modelling |
Xiaoli Kong,
Jing Dai |
source
presentation source |
8 |
| PHUSE US |
2024 |
From stowaway to admiral, a R-iveting tale of a hierarchical composite endpoint's journey from idea to dataset |
Nicole Major,
Otto Magnusson |
source
presentation source |
16 |
| PHUSE US |
2024 |
It is Time to Get Answers: Handling Questionnaire Data in Respiratory Studies |
Valeriia Oreshko |
source
presentation source |
10 |
| PHUSE US |
2024 |
Programming Approaches for Integrated Summary of Efficacy (ISE) Development in Oncology |
Narendra Babu Kolisetty,
Saigovind Chenna |
source
presentation source |
7 |
| PHUSE US |
2024 |
Risk-Based Monitoring Using AI/ML Detection of Systematic Bias in Clinical Data |
Kostiantyn Drach,
Sergey Glushakov |
source
presentation source |
12 |
| PHUSE US |
2024 |
SAS PROC MI: Your Impeccable Tool for Enhanced Data Analysis Through the Lens of Multiple Imputation |
Anush Kurghinyan,
Shahrzad Talebian |
source
presentation source |
7 |
| PHUSE EU |
2023 |
A Beginner's Guide to Overcome the Attrition Bias in Clinical Research: The Method of Inverse Probability of Attrition Weighting (IPAW) |
Parichay Srivastava |
source
presentation source |
9 |
| PHUSE EU |
2023 |
Comparing Analysis Method Implementations in Software (CAMIS): An Open Source Repository to Document Differences in Statistical Methodology Across Software |
Christina Fillmore |
source
presentation source |
7 |
| PHUSE EU |
2023 |
Detroit, Michigan: A Case Study for DEI Via Decentralised Clinical Trials (DCTs) |
Mark Rothe |
source
presentation source |
26 |
| PHUSE EU |
2023 |
Efficacy Analyses with Imputations: Methods, Limitations and Lessons Learned |
Amine Khemiri |
source
presentation source |
3 |
| PHUSE EU |
2023 |
Handling Missing Data in Exploratory Analyses Using RWD: 'Collect the Data You Love or Love the Data You Get' - A Case Study [presentation] |
Jean-Marc Ferran |
source |
22 |
| PHUSE EU |
2023 |
Mr. ight - Why R Should Be Used for Validating TLFs in Clinical Trials? |
Monika Ludwinska |
source
presentation source |
10 |
| WUSS |
2023 |
10 Quick Tips for Getting Tipsy with SAS |
Lisa A. Mendez,
Richann J. Watson |
source |
16 |
| WUSS |
2023 |
Application of Fuzzy Matching Techniques Using SAS Software |
Kirk Paul Lafler,
Stephen B. Sloan |
source |
30 |
| WUSS |
2023 |
Introduction to Machine Learning - Descriptions and Best Practices |
Jim Box |
source |
13 |
| WUSS |
2023 |
Make You Holla' Tikka Masala: Creating User-Defined Informats Using the PROC FORMAT OTHER Option To Call User-Defined FCMP Functions That Facilitate Data Ingestion Data Quality |
Troy Martin Hughes |
source |
10 |
| WUSS |
2023 |
Regression Analysis Made Easy Using SAS Studio |
Kirk Paul Lafler,
Zheyuan (Walter) Yu,
Nuoer Lu,
Nicklas (Rebel) Yee,
Yanzhang Gavin Chen,
Kai Kang |
source |
43 |
| WUSS |
2023 |
Sorting a Bajillion Variables: When SORTC and SORTN Subroutines Have Stopped Satisfying, User-Defined PROC FCMP Subroutines Can Leverage the Hash Object to Reorder Limitless Arrays |
Troy Martin Hughes |
source |
16 |
| WUSS |
2023 |
The Essentials of SAS Dates and Times |
Derek P. Morgan |
source |
24 |
| WUSS |
2023 |
Unraveling the Layers within Neural Networks: Designing Artificial and Convolutional Neural Networks for Classification and Regression Tasks using TensorFlow in Python |
Ryan Paul Lafler,
Anna T. K. Wade |
source |
24 |
| SESUG |
2023 |
10 Quick Tips for Getting Tipsy with SAS |
Richann J. Watson,
Lisa A. Mendez |
source |
16 |
| SESUG |
2023 |
A Quick Look at Fuzzy Matching Programming Techniques Using SAS Software |
Stephen B. Sloan,
Kirk Paul Lafler |
source |
29 |
| SESUG |
2023 |
Application of Fuzzy Matching Techniques Using SAS Software |
Kirk Paul Lafler,
Stephen B. Sloan |
source |
30 |
| SESUG |
2023 |
Best Practices for Efficiency and Code Optimization in SAS programming |
Jayanth Iyengar |
source |
20 |
| SESUG |
2023 |
Looking for the Missing(ness) Piece |
Louise S. Hadden |
source |
4 |
| SESUG |
2023 |
Looking for the Missing(ness) Piece |
Louise S. Hadden |
source |
4 |
| SESUG |
2023 |
Make You Holla' Tikka Masala: Creating User-Defined Informats Using the PROC FORMAT OTHER Option To Call User-Defined FCMP Functions That Facilitate Data Ingestion Data Quality |
Troy Martin Hughes |
source |
10 |
| SESUG |
2023 |
MISSING Mysteries for SAS Beginners |
Jinson J. Erinjeri |
source |
9 |
| SESUG |
2023 |
Regression Analysis Made Easy Using SAS Studio |
Kirk Paul Lafler,
Zheyuan (Walter) Yu,
Nuoer Lu,
Daniel Qian,
Kai Kang,
Yanzhang Gavin Chen,
Nicklas (Rebel) Yee,
Yixuan (Jason) Xiang |
source |
43 |
| SESUG |
2023 |
SAS Macro for Randomization-Based Methods for Covariance and Stratified Adjustment of Win Ratios and Win Odds for Ordinal Outcomes |
Elaine Kowalewski,
Ann Marie K. Weideman,
Gary G. Koch |
source |
11 |
| SESUG |
2023 |
SAS Program Efficiency for Beginners |
Bruce F. Gilsen |
source |
15 |
| SESUG |
2023 |
Sorting a Bajillion Variables: When SORTC and SORTN Subroutines Have Stopped Satisfying, User-Defined PROC FCMP Subroutines Can Leverage the Hash Object to Reorder Limitless Arrays |
Troy Martin Hughes |
source |
16 |
| SESUG |
2023 |
Wald Tests in Mixed Models - a Warning |
David A. Dickey |
source |
20 |
| PharmaSUG |
2023 |
10 Quick Tips for Getting Tipsy with SAS |
Lisa A. Mendez,
Richann J. Watson |
source |
16 |
| PharmaSUG |
2023 |
A Quick Look at Fuzzy Matching Programming Techniques Using SAS Software |
Stephen B. Sloan,
Kirk Paul Lafler |
source |
29 |
| PharmaSUG |
2023 |
Application of Tipping Point Analysis in Clinical Trials using the Multiple Imputation Procedure in SAS |
Yunxia Sui,
Xianwei Bu,
Yuanyuan Duan,
Yihan Li,
Xin Wang |
source |
12 |
| PharmaSUG |
2023 |
Automating SDTM: A metadata-driven journey. |
Keith Hibbetts |
source |
11 |
| PharmaSUG |
2023 |
Best Practices for Efficiency and Code Optimization in SAS programming |
Jayanth Iyengar |
source |
20 |
| PharmaSUG |
2023 |
Make You Holla' Tikka Masala: Creating User-Defined Informats Using the PROC FORMAT OTHER Option To Call User-Defined FCMP Functions That Facilitate Data Ingestion Data Quality |
Troy Martin Hughes |
source |
10 |
| PharmaSUG |
2023 |
Real Projects, Real Transition, Really Revolutionary - Transitioning to R for Biometrics Work |
Danielle Stephenson,
Rebekah Oster,
Alyssa M. Wittle |
source |
11 |
| PharmaSUG |
2023 |
Sensitivity Analysis for Missing Data Using Control-based Pattern Imputation |
Jun Feng,
Jingmin Liu |
source |
7 |
| PharmaSUG |
2023 |
Sorting a Bajillion Variables: When SORTC and SORTN Subroutines Have Stopped Satisfying, User-Defined PROC FCMP Subroutines Can Leverage the Hash Object to Reorder Limitless Arrays |
Troy Martin Hughes |
source |
16 |
| PharmaSUG |
2023 |
Statistical Considerations and Methods for Handling Missing Outcome Data in Clinical Trials During the Era of COVID-19 |
Xi Qian,
Chengfei Lu |
source |
11 |
| PharmaSUG |
2023 |
The Phantom of the ADaM: adding missing records to BDS datasets |
Anastasiia Drach |
source |
10 |
| PharmaSUG |
2023 |
Using R to Create Population Pharmacokinetic Dataset |
Yangwei Yan,
Prema Sukumar,
Neelima Thanneer |
source |
10 |
| PharmaSUG |
2023 |
Using the R interface in SAS to Call R Functions and Transfer Data |
Bruce F. Gilsen |
source |
16 |
| PharmaSUG |
2023 |
Was the load okay? |
Lisa Eckler,
Lisa Eckler |
source |
22 |
| PharmaSUG |
2023 |
What is Machine Learning, anyway? |
Jim Box |
source |
11 |
| PHUSE US |
2023 |
Conditional Branching in QRS Data and How to Represent It [presentation] |
Dana Booth,
Steve Kopko |
source
video |
39 |
| PHUSE US |
2023 |
Performing Propensity-score-integrated Comparative Analysis in External Control Arm Studies Using R [presentation] |
Lixiao Chen,
Lorraine Fang |
source
video |
20 |
| PHUSE US |
2023 |
RBM and Failure Prediction Using Python for Health Monitoring Devices |
Vidya Muthukumar |
source
presentation source |
7 |
| PHUSE US |
2023 |
SPECification DOCument (SPECDOC): Automating Text for Data Description, Statistical Methods, and Analysis Results for Medical Communications [presentation] |
Michael Pannucci |
source |
20 |
| PHUSE US |
2023 |
Staying Alert to Data Quality: A Shiny App to Visualise the Electronic Patient Reported Outcome (ePRO) Data |
Wenyi Liu |
source
presentation source |
6 |
| PHUSE US |
2023 |
The Thick and Thin of Adopting Risk-Based Approach to Data Management [presentation] |
Michał Ruciński |
source
video |
17 |
| PHUSE US |
2023 |
Tidytlg: An R Package for Clinical Reporting Using Tidyverse |
Sheng-Wei Wang |
source
presentation source |
9 |
| PHUSE EU |
2022 |
Functional Programming: What, Why and How? |
Tom Ratford |
source
presentation source |
8 |
| PHUSE EU |
2022 |
Handling Missing Data in Statistical Programming Using Multiple Imputations in SAS [presentation] |
Marta Tomczyk,
Martyna Gawlowska |
source
video |
15 |
| PHUSE EU |
2022 |
Harnessing Routinely Generated git, github and Package-specific Metadata to Build a Portal into Open-source Projects [presentation] |
Mathieu Cayssol,
James Black |
source
video |
18 |
| PHUSE EU |
2022 |
My Journey from SAS to R [presentation] |
Lucy Watson |
source |
15 |
| PHUSE EU |
2022 |
Python to Automate an AR Project Identifying Discrepancies across Fields from a Government Open-source Dataset of COVID-19 Vaccine Registrations |
Aldir Medeiros Filho |
source
video
presentation source |
14 |
| PHUSE EU |
2022 |
SanityCheckR: data quality control with R Shiny |
Diana L. Santos Ferreira |
source
poster source |
5 |
| PHUSE EU |
2022 |
Tumour Assessment Evaluation Using MS Azure and Power BI [presentation] |
Robert Adams,
Clara Beck |
source
video |
22 |
| SESUG |
2022 |
Application of Fuzzy Matching Techniques Using SAS Software - A Panel Discussion |
Kirk Paul Lafler,
Stephen B. Sloan |
source |
30 |
| SESUG |
2022 |
Essential Programming Techniques Every SAS User Should Learn |
Kirk Paul Lafler |
source |
27 |
| SESUG |
2022 |
Functions (and More!) on CALL! |
Richann J. Watson,
Louise S. Hadden |
source |
33 |
| SESUG |
2022 |
Offensive Programming: A Threesome of Error-Throwing Macros |
Quentin McMullen |
source |
20 |
| SESUG |
2022 |
To Lend, or Not to Lend, That Is the Question! Evaluating Loan Application with Machine Learning |
Rafae Abdullah,
Sucharitha Vallabhaneni,
Jaideep Muley |
source |
17 |
| WUSS |
2022 |
A Survey of Some of the Most Useful SAS Functions |
Ronald P. Cody |
source |
16 |
| WUSS |
2022 |
Analysis of Chemicals in Beauty Products and its Impact on Consumers |
Chhavi Nijhawan |
source |
8 |
| WUSS |
2022 |
Application of Fuzzy Matching Techniques Using SAS Software - A Panel Discussion |
Kirk Paul Lafler,
Stephen B. Sloan |
source |
30 |
| WUSS |
2022 |
Attribute Reduction for Continuous Dependent Variables in SAS |
Alec Zhixiao Lin |
source |
20 |
| WUSS |
2022 |
Best Practices for Efficiency and Code Optimization in SAS programming |
Jayanth Iyengar |
source |
21 |
| WUSS |
2022 |
Did the load work? |
Lisa Eckler |
source |
18 |
| WUSS |
2022 |
Essential Programming Techniques Every SAS User Should Learn |
Kirk Paul Lafler |
source |
27 |
| WUSS |
2022 |
Functions (and More!) on CALL! |
Richann J. Watson,
Louise S. Hadden |
source |
33 |
| WUSS |
2022 |
The Essentials of SAS Dates and Times |
Derek P. Morgan |
source |
25 |
| PharmaSUG |
2022 |
A Programmer's Perspective on Patient reported Outcomes in Oncology Trials |
Divya Vemulapalli |
source |
7 |
| PharmaSUG |
2022 |
A Quick Look at Fuzzy Matching Programming Techniques Using SAS Software |
Stephen B. Sloan,
Kirk Paul Lafler |
source |
29 |
| PharmaSUG |
2022 |
Begin with the End in Mind: eCOA data from collection to analysis |
Franklin E. Menius,
Monali Khanna,
Vincent Allibone |
source |
7 |
| PharmaSUG |
2022 |
Digital Health Technology in Clinical Trials: Opportunities, Challenges and Biometrics Future Re-imagined |
Kalyan Chakravarthy Buddavarapu,
Sherry Ye,
Vijay Vaidya |
source |
12 |
| PharmaSUG |
2022 |
Functions (and More!) on CALL! |
Richann J. Watson,
Louise S. Hadden |
source |
33 |
| PharmaSUG |
2022 |
Unravel the mystery around NONMEM data sets for statistical programmers |
Sabarinath Sundaram,
Lyma Faroz |
source |
9 |
| PharmaSUG |
2022 |
Working with Dataset-JSON using SAS |
Lex Jansen |
source |
23 |
| PHUSE US |
2022 |
Reassure Your Assumptions Using Sensitivity Analysis [presentation] |
Vadym Kalinichenko |
source
video |
29 |
| PHUSE US |
2022 |
The Complexity and Magnitude of Participant Reactogenicity Data [presentation] |
Gunasekaran Subramaniam,
Katrien Van der Donck |
source
video |
21 |
| PHUSE EU |
2021 |
Does No Errors Equal High Quality? [presentation] |
Julie Solbjerg Appel,
Sanna Herrgård |
source
video |
23 |
| PHUSE EU |
2021 |
Metadata: Errors Not Detected by the Pinnacle 21 Tool [presentation] |
Caroline Pelckmans,
Pamphile Beaujean |
source
video |
20 |
| PHUSE EU |
2021 |
Programming Infrastructure to Automate Process Flow for CSS [presentation] |
Archana Nayak,
Salvatore Saporito |
source
video |
22 |
| PHUSE EU |
2021 |
Standard Implementation: Battling Through the Adversity of a Pandemic [presentation] |
Ana Arques,
Vicky Poulsen |
source
video |
18 |
| SESUG |
2021 |
A Quick Look at Fuzzy Matching Programming Techniques Using SAS Software |
Stephen B. Sloan,
Kirk Paul Lafler |
source |
28 |
| SESUG |
2021 |
Back to Basics: Running an Analysis from Data to Refinement in SAS |
Deanna Naomi Schreiber-Gregory |
source |
21 |
| SESUG |
2021 |
Finding variable names and count of variables with missing values at various positions within an observation using SAS arrays |
Kiran Venna |
source |
6 |
| SESUG |
2021 |
Looking for the Missing(ness) Piece |
Louise S. Hadden |
source |
4 |
| SESUG |
2021 |
Missing Value Imputation |
Ross Bettinger |
source |
34 |
| SESUG |
2021 |
Moving beyond Frequency and Percentage to Chi Square, t-Tests, and Correlation Analysis |
Kelly D. Smith |
source |
12 |
| SESUG |
2021 |
Use SAS Enterprise Miner Workstation 15.1 to Do Predictive Analysis for Mobile Strategy Games Industry |
Chun Du |
source |
11 |
| SESUG |
2021 |
Yo Mama is Broke Cause Yo Daddy is Missing: Autonomously and Responsibly Responding to Missing or Invalid SAS Data Sets Through Exception Handling Routines |
Troy Martin Hughes |
source |
11 |
| PHUSE US |
2021 |
COVID-19 Presentation: CO01: Harnessing the Power of Shiny to Visualize Missing Data due to the COVID-19 Pandemic [presentation] |
Mark Rothe,
Sangeeta Bhattacharya |
source |
13 |
| PHUSE US |
2021 |
Digitalization, Agile Mindset and Virtual Collaboration - Yet Delivering a "Pile of Paper"? [presentation] |
Jesper Zeth,
Parag Wani |
source
video |
17 |
| PHUSE US |
2021 |
Industry Metrics for Extensions of CDISC Terminology [presentation] |
Sergiy Sirichenko |
source
video |
36 |
| PHUSE US |
2021 |
Using Central Monitoring to Mitigate Data Quality Risk during the COVID-19 Pandemic [presentation] |
Adrienne Strickler,
Anna Wojcik |
source
video |
21 |
| PharmaSUG |
2021 |
A Quick Look at Fuzzy Matching Programming Techniques Using SAS Software |
Stephen B. Sloan,
Kirk Paul Lafler |
source |
28 |
| PharmaSUG |
2021 |
Adding Rows to a BDS Dataset: When to Use DTYPE in Addition to Metadata |
Sandra Minjoe |
source |
11 |
| PharmaSUG |
2021 |
Back to Basics: Running an Analysis from Data to Refinement in SAS |
Deanna Naomi Schreiber-Gregory |
source |
21 |
| PharmaSUG |
2021 |
Essential Programming Techniques Every SAS User Should Learn |
Kirk Paul Lafler |
source |
26 |
| PharmaSUG |
2021 |
Forward Planning: How to get the most of your eCOA data |
Monali Khanna,
Franklin E. Menius |
source |
7 |
| PharmaSUG |
2021 |
Looking for the Missing(ness) Piece |
Louise S. Hadden |
source |
4 |
| PharmaSUG |
2021 |
Yo Mama is Broke Cause Yo Daddy is Missing: Autonomously and Responsibly Responding to Missing or Invalid SAS Data Sets Through Exception Handling Routines |
Troy Martin Hughes |
source |
11 |
| SGF |
2021 |
Back to Basics: Running an Analysis from Data to Refinement in SAS |
Deanna Naomi Schreiber-Gregory |
source
presentation |
20 |
| SGF |
2021 |
Do Americans Trust Scientific Experts? |
Jaswinder Singh,
Kalyan Alisetty,
Vinil Rayala,
Taylor Rebollar |
source |
7 |
| SGF |
2021 |
Investing in Education Intelligently |
Sam Edison,
Rushya Puttam,
Akhil Emani,
Nithisha Katta |
source
presentation |
7 |
| SGF |
2021 |
Making SAS Tables Smaller and Faster Without Data Loss |
Glen Becker |
source
presentation |
16 |
| SGF |
2021 |
Modernizing Scenario Analysis With SAS Viya and SAS Visual Analytics |
Arne Cordes,
Stu Sztukowski |
source
presentation |
21 |
| SGF |
2021 |
The SAS Data Set Characterization Utility |
Michael A. Raithel |
source |
10 |
| PHUSE EU |
2020 |
Getting It Right: Refinement of SEND Validation Rules [presentation] |
Kristin Kelly |
source
video |
29 |
| PHUSE EU |
2020 |
Incomplete Data in Longitudinal Studies: Continuous and Dichotomous Outcome [presentation] |
Ana Grecu |
source
video |
20 |
| PHUSE EU |
2020 |
MI FOR MI, OR HOW TO HANDLE MISSING INFORMATION WITH MULTIPLE IMPUTATION |
Daria Shamaida |
source
video
presentation source |
8 |
| SESUG |
2020 |
Controlling the Order of Data in SAS |
Imelda C. Go |
source |
9 |
| SESUG |
2020 |
The Efficient Alternative to LAG Function without LAG's Pitfalls |
Alexander M. Feigin |
source |
8 |
| SESUG |
2020 |
Upgrading Clinical Trial Reports from ODS LISTING to ODS TAGSETS.RTF |
Christopher J. Smith |
source |
25 |
| SGF |
2020 |
A Warning about Wald Tests |
David A. Dickey |
source |
20 |
| SGF |
2020 |
Analyzing Non-normal Data: Application to Missing Data Problems |
Niloofar Ramezani |
source |
10 |
| SGF |
2020 |
Analyzing the Factors Impacting Suicidal Behavior in American Youth |
Ashlesha Sampathi |
source |
7 |
| SGF |
2020 |
Creating Custom Microsoft Excel Workbooks Using the SAS Output Delivery System, Part 1 |
Vincent DelGobbo |
source |
33 |
| SGF |
2020 |
Dealing with Missing Data in Epidemiological and Clinical Research |
Andrew T. Kuligowski,
Lida Gharibvand |
source |
6 |
| SGF |
2020 |
Economic Impacts of Sea Level Rise on Coastal Real Estate |
Austin Willoughby |
source |
9 |
| SGF |
2020 |
From Santiago to the Spirit Lake Nation: 30 Things I Learned in 30 Years as a Statistical Consultant |
AnnMaria De Mars |
source |
20 |
| SGF |
2020 |
Make Your DO Loop More Efficient |
G Liu |
source |
4 |
| SGF |
2020 |
Making Life Easier on the SAS End: Best Practices for |
Julie Plano,
Keli Sorrentino |
source |
20 |
| SGF |
2020 |
Moving from Messy Data to a Clean Analytic Dataset: Common Data Management Techniques using SAS |
Bethany A. Bell |
source |
27 |
| SGF |
2020 |
Multinomial vs. Ordinal. Does model selection make a difference? for SAS Global Forum 2020 |
Corey Leadbeater |
source |
15 |
| SGF |
2020 |
Neural-Network Based Forecasting Strategies in SAS Viya |
Steven C. Mills |
source |
14 |
| SGF |
2020 |
Reducing Ecological Footprint: Examining Panel Data |
Neil Belford |
source |
7 |
| SGF |
2020 |
Smarter and Faster Self-Service Data Preparation |
Atrin Assa |
source |
11 |
| SGF |
2020 |
Some _FILE_ Magic |
Michael S. Zdeb |
source |
7 |
| SGF |
2020 |
The Essentials of SAS Dates and Times |
Derek P. Morgan |
source |
25 |
| SGF |
2020 |
Think Globally, Act Locally: Understanding the Global and Local Macro Symbol Tables |
Michelle Buchecker |
source |
11 |
| SGF |
2020 |
UPDATE to the Rescue |
Robert Virgile |
source |
9 |
| SGF |
2020 |
Upgrading Clinical Trial Reports from ODS LISTING to ODS TAGSETS.RTF |
Christopher J. Smith,
Joshua M. Horstman |
source |
25 |
| SGF |
2020 |
Urge to MERGE? Maybe You Should UPDATE Instead |
Ben Cochran |
source |
7 |
| SGF |
2020 |
Using Factor Analysis and MANOVA to Explore Academic Achievement in the 2016 Monitoring the Future Survey Data |
Stefanie Kairs |
source |
20 |
| SGF |
2020 |
Using Python with Model Studio for SAS Visual Data Mining and Machine Learning |
Jagruti Kanjia |
source |
35 |
| SGF |
2020 |
Using the R interface in SAS to Call R Functions and Transfer Data |
Bruce F. Gilsen |
source |
17 |
| PharmaSUG |
2020 |
Fuzzy Matching Programming Techniques Using SAS Software |
Stephen B. Sloan,
Kirk Paul Lafler |
source |
28 |
| PharmaSUG |
2020 |
Generating ADaM compliant ADSL Dataset by Using R |
Vipin Kumpawat,
Lalitkumar Bansal |
source |
9 |
| PharmaSUG |
2020 |
Great Time to Learn GTL: A Step-by-Step Approach to Creating the Impossible |
Richann J. Watson |
source |
26 |
| PharmaSUG |
2020 |
How to Achieve More with Less Code |
Timothy J. Harrington |
source |
13 |
| PharmaSUG |
2020 |
Yo Mama is Broke Cause Yo Daddy is Missing: Autonomously and Responsibly Responding to Missing or Invalid SAS Data Sets Through Exception Handling Routines |
Troy Martin Hughes |
source |
11 |
| PHUSE US |
2020 |
Challenges and Solutions While Mapping Laboratory Data [poster] |
Ratheesh Gunda |
source |
1 |
| PHUSE US |
2020 |
Data Review of RECIST Criteria using ML/AI A Proof of Concept Example |
Priya Gopal |
source
presentation source |
7 |
| PHUSE US |
2020 |
Generating TFLs in R - Challenges and Successes Compared to SAS |
Amol Wayker,
Andrew Miskell |
source
presentation source |
10 |
| PHUSE US |
2020 |
Handling Duplicate Records in SDTM for Effective Resolution and Explanation in Data Submission |
Varun Debbeti |
source
presentation source |
8 |
| PHUSE US |
2020 |
Handling Missing Data in Clinical Trials: Techniques and Methods |
Pennidhi Karlakunta,
Naveen Kommuru |
source
presentation source |
9 |
| PHUSE US |
2020 |
How to reduce programming time for ADaM Creation? - Presenting STAG, a Tool Based Approach |
Diganta Bose |
source
presentation source |
16 |
| PHUSE EU |
2019 |
Beyond missing data patterns: augmenting PROC MI tables with summary of imputed data |
Lamine Signate |
source
presentation source |
6 |
| PHUSE EU |
2019 |
Mixed Model Repeated Measures (MMRMs) |
Mrudula Suryawanshi |
source
presentation source |
19 |
| PHUSE EU |
2019 |
Multiple imputation as a valid way of dealing with missing data |
Vadym Kalinichenko |
source
presentation source |
11 |
| PHUSE EU |
2019 |
Sub-population Detection Using Graph-based Machine Learning |
Sergey Glushakov,
Kostiantyn Drach |
source |
28 |
| PHUSE EU |
2019 |
The Promise of Wearable Technology in Clinical Trials: A Proof of Concept to Deliver it? |
Jennifer Bradford |
source
presentation source |
11 |
| SESUG |
2019 |
A user-friendly and robust macro that produces a publication-ready Table 1 |
Daniel Brinton,
Marshall Chew |
source |
5 |
| SESUG |
2019 |
Examining Imputation effect by Using FACTOR and MI Procedures in SAS for a Bullying Scale for LGBTQ Youth in SC |
Examining Imputation |
source |
5 |
| SESUG |
2019 |
Fifteen Functions to Supercharge Your SAS Code |
Joshua M. Horstman |
source |
8 |
| SESUG |
2019 |
Great Time to Learn GTL: A Step-by-Step Approach to Creating the Impossible |
Richann J. Watson |
source |
26 |
| SESUG |
2019 |
Macros for Neural Network Modeling |
Ross Bettinger |
source |
23 |
| SESUG |
2019 |
PROC SQL for PROC SUMMARY Stalwarts |
Christianna S. Williams |
source |
18 |
| SESUG |
2019 |
SAS Formats Top Ten |
Christianna S. Williams |
source |
17 |
| SESUG |
2019 |
Using PROC TABULATE and ODS Style Options to Make Really Great Tables |
Wendi L. Wright |
source |
19 |
| SESUG |
2019 |
Using the R interface in SAS to Call R Functions and Transfer Data |
Bruce F. Gilsen |
source |
17 |
| SESUG |
2019 |
Validating Classification and Recidivism in Corrections Through Outcome Analytics |
Alan R. Mann |
source |
34 |
| MWSUG |
2019 |
Classifying Risk in Life Insurance using Predictive Analytics |
Sai Gopi Krishna Govindarajula |
|
6 |
| MWSUG |
2019 |
Fifteen Functions to Supercharge Your SAS Code |
Joshua M. Horstman |
|
8 |
| MWSUG |
2019 |
Sample Size and Design Considerations in Studies Assessing Non-Inferiority using Continuous Outcomes |
Michael G. Wilson |
|
19 |
| MWSUG |
2019 |
Urge to MERGE? Maybe You Should UPDATE Instead |
Ben Cochran |
|
8 |
| WUSS |
2019 |
A Quick Look at Fuzzy Matching Programming Techniques Using SAS Software |
Kirk Paul Lafler,
Stephen B. Sloan |
source |
28 |
| WUSS |
2019 |
Fifteen Functions to Supercharge Your SAS Code |
Fifteen Functions,
Joshua M. Horstman |
source |
8 |
| WUSS |
2019 |
Great Time to Learn GTL: A Step-by-Step Approach to Creating the Impossible |
Richann J. Watson |
source |
26 |
| WUSS |
2019 |
LOAN DEFAULT PREDICTION |
Amarjeet Cheema,
Miriam McGaugh |
source |
8 |
| WUSS |
2019 |
Ordinal vs. Multinomial: Does Model Selection Make a Difference? |
Corey Leadbeater |
source |
14 |
| WUSS |
2019 |
Polynomial Regression for Modeling Curvilinear Data: A Biological Example |
Elisha Johnston |
source |
20 |
| WUSS |
2019 |
Using Factor Analysis and Multivariate Analysis of Variance to Explore Academic Achievement in the 2016 Monitoring the Future Study |
Stefanie N. Kairs |
source |
19 |
| PharmaSUG |
2019 |
An Introduction to the Process of Improving a Neural Network |
YuTing Tian |
source |
22 |
| PharmaSUG |
2019 |
Best Practices for ISS/ISE Dataset Development |
Bharath Donthi,
Lingjiao Qi |
source |
9 |
| PharmaSUG |
2019 |
Common Pinnacle 21 Report Issues: Shall we Document or Fix? |
Ajay Gupta |
source |
11 |
| PharmaSUG |
2019 |
Experiences in Building CDISC Compliant ADaM Dataset to Support Multiple Imputation Analysis for Clinical Trials |
Xiangchen (Bob) Cui |
source |
20 |
| PharmaSUG |
2019 |
Machine Learning Approaches to Identify Rare Diseases |
Xuan Sun,
Ruohan Wang |
source |
5 |
| PharmaSUG |
2019 |
Python-izing the SAS Programmer |
Mike Molter |
source |
19 |
| PharmaSUG |
2019 |
SAS V9.4 MNAR statement for multiple imputations for missing not at random in longitudinal clinical trials |
Lingling Li |
source |
11 |
| PharmaSUG |
2019 |
The Art of Defensive Programming: Coping with Unseen Data |
Philip R. Holland |
source |
8 |
| PharmaSUG |
2019 |
The Power of Data Visualization in R |
Oleksandr Babych |
source |
21 |
| PharmaSUG |
2019 |
The Power of PROC FORMAT |
Jonas V. Bilenas,
Kajal Tahiliani |
source |
13 |
| PharmaSUG |
2019 |
Unleash the Power of Less Well Known but Useful SAS DATA Step Functions |
Timothy J. Harrington |
source |
22 |
| PharmaSUG |
2019 |
Ushering SAS Emergency Medicine into the 21st Century: Toward Exception Handling Objectives, Actions, Outcomes, and Comms |
Troy Martin Hughes |
source |
24 |
| SGF |
2019 |
An Introduction to the Process of Improving a Neural Network |
YuTing Tian |
source |
22 |
| SGF |
2019 |
Choice Model Analysis Using JMP |
Swarup C. Jacob,
Marc A. Tower |
source |
6 |
| SGF |
2019 |
Cool SQL Tricks |
Russell M. Lavery |
source |
53 |
| SGF |
2019 |
Exploring the Intentions of Entering Entrepreneurship for SAS Global Forum 2019 |
Surabhi Arya,
Prashant Gour,
Dhruv Sharma,
Jeroen Vanheeringen |
source |
7 |
| SGF |
2019 |
Factors Influencing Academic Performance |
Onkar Mayekar,
Miriam McGaugh |
source |
9 |
| SGF |
2019 |
Google Store Revenue Probability Prediction |
Rohit Kaul,
Meera Govindan |
source |
10 |
| SGF |
2019 |
Got Results? Let Cloud Analytic Services Language (CASL) Help You Turn Them into Superior Reports |
Jerry C. Pendergrass |
source |
19 |
| SGF |
2019 |
Great Time to Learn GTL: A Step-by-Step Approach to Creating the Impossible |
Richann J. Watson |
source |
26 |
| SGF |
2019 |
How to Use Deep Learning with Your Internet of Things (IoT) Digital Twin |
Bradley W. Klenz |
source |
9 |
| SGF |
2019 |
Interaction between SAS and Python for Data Handling and Visualization |
Yohei Takanami |
source |
22 |
| SGF |
2019 |
Machine Learning and Predictive Analytics in SAS Enterprise Miner and SAS/STAT Software |
David Richard Cutler |
source |
21 |
| SGF |
2019 |
Macros I Use Every Day (And You Can, Too!) |
Joe DeShon |
source |
11 |
| SGF |
2019 |
Making Life Easier on the SAS End: Best Practices for Collecting Survey Data |
Julie Plano,
Keli Sorrentino |
source |
11 |
| SGF |
2019 |
Multiple Imputation for Skewed Multivariate Data: A Marriage of the MI and COPULA Procedures |
Zhixin Lun,
Ravindra Khattree |
source |
9 |
| SGF |
2019 |
Predicting Patients at High Risk of Hospital-Acquired Conditions Using the LOGISTIC Procedure |
Jennifer Hargrove,
Pamela Hipp,
Glenn Sidle |
source |
12 |
| SGF |
2019 |
SCORE! Techniques for Scoring Predictive Regression Models Using SAS/STAT Software |
Phil Gibbs,
Randy Tobias |
source |
18 |
| SGF |
2019 |
SWAT's it all about? SAS Viya for Python Users |
Carrie Foreman |
source |
18 |
| SGF |
2019 |
Ten Underused Features That Improve Your SAS Forecast Server Workflow |
Evan Anderson,
Michael J. Leonard |
source |
18 |
| SGF |
2019 |
The Aftermath What Happens after you Deploy Your Models and Decisions |
David R. Duling |
source |
18 |
| SGF |
2019 |
The Essentials of SAS Dates and Times |
Derek P. Morgan |
source |
25 |
| SGF |
2019 |
The Mother Of All Metadata Files: Using SAS Macros To Manage Metadata |
Hugh G. McCabe |
source |
14 |
| SGF |
2019 |
To Buy or to Create? Determining the Best Option for Student Success Models |
Stephanie R. Thompson |
source |
6 |
| SGF |
2019 |
Using the R interface in SAS to Call R Functions and Transfer Data |
Bruce F. Gilsen |
source |
17 |
| SGF |
2019 |
Zero-Inflated and Zero-Truncated Count Data Models with the NLMIXED Procedure |
Robin High |
source |
18 |
| PHUSE US |
2019 |
A Future 'View' of Digital Data from Wearable Devices |
Daniel Schramek |
source
presentation source |
11 |
| PHUSE US |
2019 |
Compliance Report for Patient Reported Outcomes |
Shu-Ching,
Christine S Teng |
source
presentation source |
8 |
| PHUSE US |
2019 |
Creating the Virologic Response ADaM Dataset |
Manjusha Alapati |
source
presentation source |
7 |
| PHUSE US |
2019 |
Misuse and Misapplication in Statistical Data Analysis - A Topic that Never Goes Out of Style |
Corinna Miede |
source
presentation source |
12 |
| SCSUG |
2018 |
Conditional Processing in the SAS Software by Example |
Charu Shankar |
source |
9 |
| SCSUG |
2018 |
Secret to SAS Dates |
Alison Little |
source |
9 |
| PHUSE EU |
2018 |
Forest Plots in Survival Subgroup Analyses |
Michael Brockmann,
Julia Benzing |
source
presentation source |
11 |
| PHUSE EU |
2018 |
Handling Missing Data in Clinical Trials Using Topological Data Analysis |
Sergey Glushakov,
Andrey Rekalo |
source
presentation source |
10 |
| PHUSE EU |
2018 |
Optimizing the Traceability Method in ADaM Parallel Conversions |
Roxane Debrus |
source
presentation source |
15 |
| PHUSE EU |
2018 |
Propensity Score Matching |
Stephanie Fechtner |
source
presentation source |
9 |
| SESUG |
2018 |
ANALYSIS OF FACTORS INFLUENCING DROPOUTS IN SOPHOMORE ENROLLMENT |
Apoorva Chandrasekaran |
source |
14 |
| SESUG |
2018 |
Developing In-House Student Success Models |
Stephanie R. Thompson |
source |
6 |
| SESUG |
2018 |
From CSV to SAS: Dealing with Empty Fields and Repeated Observations |
Kelly D. Smith,
Paul G. Earls |
source |
7 |
| SESUG |
2018 |
Fuzzy Matching Programming Techniques Using SAS Software |
Stephen B. Sloan |
source |
29 |
| SESUG |
2018 |
Predicting the Risk of Attrition for Undergraduate Students using SAS Enterprise Miner |
Sivateja Reddy Kandula |
source |
12 |
| SESUG |
2018 |
Sorting Arrays Using the Hash Object |
Paul M. Dorfman |
source |
17 |
| SESUG |
2018 |
Undergraduate Student Retention, Can we catch them before they fall? |
Lohit Bhandari |
source |
6 |
| SESUG |
2018 |
Using PROC TABULATE and ODS Style Options to Make Really Great Tables |
Wendi L. Wright |
source |
19 |
| SESUG |
2018 |
Using the R interface in SAS to Call R Functions and Transfer Data |
Bruce F. Gilsen |
source |
17 |
| MWSUG |
2018 |
A Quick Look at Fuzzy Matching Programming Techniques Using SAS Software |
Kirk Paul Lafler,
Stephen B. Sloan |
|
28 |
| MWSUG |
2018 |
An Introduction to the Process of Improving a Neural Network |
YuTing Tian |
|
22 |
| MWSUG |
2018 |
Data-Driven Data Analysis |
Jack N. Shoemaker |
|
10 |
| MWSUG |
2018 |
Handling Missing Data in Exploratory Factor Analysis Using SAS |
Min Chen |
|
3 |
| WUSS |
2018 |
A Survey of Some of the Most Useful SAS Functions |
Ronald P. Cody |
source |
16 |
| WUSS |
2018 |
Crime in the USA: Using SAS to Analyze Recidivism Rates |
Philip Mayevskiy |
source |
8 |
| WUSS |
2018 |
Data-Driven Data Analysis |
Jack N. Shoemaker |
source |
10 |
| WUSS |
2018 |
Defensive programming techniques for ongoing clinical studies |
Bharath Donthi,
Lingjiao Qi |
source |
8 |
| WUSS |
2018 |
Essential Programming Techniques Every SAS User Should Learn |
Kirk Paul Lafler |
source |
26 |
| WUSS |
2018 |
How Best to Use Macro Quoting Functions |
Arthur X. Li |
source |
11 |
| WUSS |
2018 |
How sensitive is your analysis? A case study on addressing it at ADaM level. |
Bhargav Koduru,
Balavenkata Pitchuka |
source |
7 |
| WUSS |
2018 |
The Missing Link: A Robust Macro for Recoding General Missing Data Values |
Hunter Glanz,
Joshua M. Horstman |
source |
6 |
| WUSS |
2018 |
Use of PROC Tabulate Out File to Customize Tables |
Abdus Shahid |
source |
5 |
| WUSS |
2018 |
Validating User-Submitted Data Files with Base SAS |
Michael A. Raithel |
source |
16 |
| PHUSE US |
2018 |
A Statistics-based Tool to Inform Risk-based Monitoring Approaches |
Laura Williams,
Giulia Zardi |
source
presentation source |
8 |
| PHUSE US |
2018 |
Alternative Programming Approach for Conditional Processing in SAS |
Balram Chauhan |
source
presentation source |
6 |
| PHUSE US |
2018 |
Assuring Data Integrity and Data Quality in Sponsor Submissions |
Paul Schuette |
source
presentation source |
5 |
| PHUSE US |
2018 |
How to Prepare High Quality Metadata for Submission [presentation] |
Varun Debbeti |
source |
31 |
| PHUSE US |
2018 |
Methods for Handling Concentration Values Below the Limit of Quantification in PK Studies |
James R. Johnson |
source
presentation source |
9 |
| PHUSE US |
2018 |
Missing Data Imputation and Its Effect on the Accuracy of Classification |
Edwin Ponraj Thangarajan,
Giri Balasubramanian |
source
presentation source |
15 |
| PHUSE US |
2018 |
Modeling and Text Analysis to Empower FAERS Adverse Event Mitigation |
Qais Hatim,
Emily McRae |
source
presentation source |
24 |
| PHUSE US |
2018 |
SAS Programming is Done by Human Beings |
Ray Mckinnis |
source
presentation source |
4 |
| PharmaSUG |
2018 |
A time saving approach to track data issues |
Aishhwaryapriya Elamathivadivambigai |
source |
8 |
| PharmaSUG |
2018 |
Combining Electronic Diary Seizure Data with Visit-Based Seizure Data in the MONEAD Study |
Ryan May,
Julia Skinner |
source |
8 |
| PharmaSUG |
2018 |
Discover the Deeper DATA Step |
Timothy J. Harrington |
source |
14 |
| PharmaSUG |
2018 |
Fifteen Functions to Supercharge Your SAS Code |
Joshua M. Horstman |
source |
8 |
| PharmaSUG |
2018 |
R 101: The base Package |
Arthur X. Li |
source |
22 |
| PharmaSUG |
2018 |
The Fundamentals of Macro Quoting Functions |
Arthur X. Li |
source |
10 |
| PharmaSUG |
2018 |
Using SAS GTL to Visualize Data When there is Too Much of It to Visualize |
Nathaniel Derby,
Perry Watts |
source |
22 |
| PharmaSUG |
2018 |
What makes a good QC Programmer? |
Timothy J. Harrington |
source |
8 |
| SGF |
2018 |
A Cautionary Tale of Using Machine Learning within Credit Risk in the Utilities Industry |
Spiros Potamitis,
Paul Malley |
source |
8 |
| SGF |
2018 |
A Macro for Last Observation Carried Forward |
Jonson Jiang |
source |
5 |
| SGF |
2018 |
A Survey of Some of the Most Useful SAS Functions |
Ronald P. Cody |
source |
16 |
| SGF |
2018 |
Confirmatory Factor Analysis in SAS : Application to Public Administration |
Niloofar Ramezani |
source |
13 |
| SGF |
2018 |
Distribution Circuit Load Forecasting Using Advanced Metering Infrastructure Data |
Prasenjit Shil,
Tom Anderson |
source |
12 |
| SGF |
2018 |
Enterprise, Prepare for Analysis! Using SAS Data Management to Prepare Data for Analysis |
Bob Janka |
source |
17 |
| SGF |
2018 |
Evaluation of Different Approaches to Reject Inference: a case study in Credit Risk |
Sergiu Luca |
source |
9 |
| SGF |
2018 |
Fuzzy Matching Programming Techniques Using SAS Software |
Stephen B. Sloan,
Kirk Paul Lafler |
source |
24 |
| SGF |
2018 |
Macros I Use Every Day (And You Can, Too!) |
Joe DeShon |
source |
9 |
| SGF |
2018 |
Make SAS Enterprise Miner Play Nicely with SAS Viya |
Beth Ebersole |
source |
31 |
| SGF |
2018 |
Navigating the Analytics Life Cycle with SAS Visual Data Mining and Machine Learning |
Susan Haller,
Brett Wujek |
source |
29 |
| SGF |
2018 |
Parallel Programming with the DATA Step: Next Steps |
David Bultman,
Jason Secosky |
source |
16 |
| SGF |
2018 |
PROC FCMP: A Powerful SAS Procedure You Should Be Using |
Bill McNeill |
source |
17 |
| SGF |
2018 |
Run-Time Validation of SAS Jobs: A Threesome of Error-Throwing Macros |
Quentin McMullen |
source |
18 |
| SGF |
2018 |
Simple Methods for Repeatability and Comparability: Bland-Altman Plots, Bias, and Measurement Error |
Maribeth H. Johnson,
Jennifer L. Waller |
source |
12 |
| SGF |
2018 |
The Art of Defensive Programming |
Philip R. Holland |
source |
9 |
| SGF |
2018 |
Unleashing the Power of SAS Visual Analytics: Warranty Analytics in the Automobile Industry |
Prateek Kumar Singh,
Saket Shubham,
Shiv Kumar,
Paresh Peter Rodrigues |
source |
16 |
| SGF |
2018 |
Using SAS for Multiple Imputation and Analysis of Longitudinal Data |
Patricia A. Berglund |
source |
24 |
| SGF |
2018 |
Validating User-Submitted Data Files with Base SAS |
Michael A. Raithel |
source |
16 |
| SESUG |
2017 |
A Software Toolkit for Data Management |
Kim Chantala,
Jean Robinson,
Helen Smith |
source |
19 |
| SESUG |
2017 |
Applying IFN and IFC Functions |
Jinson J. Erinjeri |
source |
7 |
| SESUG |
2017 |
Beyond IF THEN ELSE: Techniques for Conditional Execution of SAS Code |
Joshua M. Horstman |
source |
10 |
| SESUG |
2017 |
Fuzzy Matching Programming Techniques Using SAS Software |
Stephen B. Sloan,
Kirk Paul Lafler |
source |
25 |
| SESUG |
2017 |
Leads and Lags: Static and Dynamic Queues in the SAS DATA STEP, 2nd ed. |
Mark Keintz |
source |
15 |
| SESUG |
2017 |
Missing Data and Complex Sample Surveys Using SAS: The Impact of Listwise Deletion vs. Multiple Imputation Methods on Point and Interval Estimates when Data are MCAR, MAR, and MNAR |
Listwise Deletion |
source |
26 |
| SESUG |
2017 |
Tales from the Help Desk: Solutions to Common DATA Step Tasks |
Bruce F. Gilsen |
source |
19 |
| SESUG |
2017 |
Using SAS to Employ Propensity Score Matching in an Institutional Research Office to Create Matched Groups for Outcomes Analyses |
Bobbie E. Frye,
James E. Bartlett |
source |
19 |
| SCSUG |
2017 |
Analysis of the effects of financial parameters on GDP and poverty using panel data analysis. |
Akshay Arora |
source |
13 |
| SCSUG |
2017 |
Analyzing Theft Occurrences in Chicago Using SAS Enterprise Miner 14.1 |
Shikha Prasad,
Kunal Parekh,
Goutam Chakraborty,
Miriam McGaugh |
source |
10 |
| SCSUG |
2017 |
Fuzzy Matching Programming Techniques Using SAS Software |
Kirk Paul Lafler,
Stephen B. Sloan |
source |
25 |
| SCSUG |
2017 |
The Good, The Bad, and The Ugly |
Toby Dunn,
Kirk Paul Lafler |
source |
12 |
| SCSUG |
2017 |
Zero My Hero! |
Anna Vincent |
source |
8 |
| PHUSE EU |
2017 |
Catching Up: Continuous Integration Pipelines for Clinical Analysis |
Andrew Karpow |
source
presentation source |
5 |
| PHUSE EU |
2017 |
Check Your Code! Control Your Data! |
Kristina Zweier |
source
presentation source |
16 |
| PHUSE EU |
2017 |
Completing the Data Set Jigsaw: Hints and Tips for Assembling Data Sets Together |
Cerys Rand |
source
presentation source |
9 |
| PHUSE EU |
2017 |
Constrain your Terminology - Use Integrity Constraints to Validate your Data |
Sven Greiner |
source
presentation source |
9 |
| PHUSE EU |
2017 |
Data Cleaning Technique for Business Analytics: What Stats Van Do for DM |
Adam Korczynski |
source
presentation source |
16 |
| PHUSE EU |
2017 |
Detecting Treatment Emergent Adverse Events (TEAEs) |
Matthias Lehrkamp |
source
presentation source |
12 |
| PHUSE EU |
2017 |
Health Technology Assessment PRO Least Squares Mean Difference Programming Techniques Using SAS |
Yirong Cao,
Siory Traoré |
source
presentation source |
11 |
| MWSUG |
2017 |
ADQRS: Basic Principles for Building Questionnaire, Rating and Scale Analysis Datasets |
Nancy Brucken,
Karin LaPann |
|
6 |
| MWSUG |
2017 |
Beyond IF THEN ELSE: Techniques for Conditional Execution of SAS Code |
Joshua M. Horstman |
|
10 |
| MWSUG |
2017 |
Data Can Be Beautiful: Crafting a Compelling Story with SAS Visual Analytics |
Cheryl L. Coyle |
|
20 |
| MWSUG |
2017 |
Demystifying Intervals |
Derek P. Morgan |
|
15 |
| MWSUG |
2017 |
Fifteen Functions to Supercharge Your SAS Code |
Joshua M. Horstman |
|
9 |
| MWSUG |
2017 |
Macro that can Provide More Information for your Character Variables |
Ting Sa |
|
7 |
| MWSUG |
2017 |
Mapping MRI data to SDTM and ADaM |
Lingling Xie,
Xiaoqi Li |
|
10 |
| MWSUG |
2017 |
Multiple Imputation of Family Income Data in the 2015 Behavioral Risk Factor Surveillance System |
Jia Li,
Aaron Sussell |
|
4 |
| MWSUG |
2017 |
The Building Blocks of SAS Datasets - S-M-U (Set, Merge, and Update) |
Andrew T. Kuligowski |
|
13 |
| MWSUG |
2017 |
The Essentials of SAS Dates and Times |
Derek P. Morgan |
|
20 |
| WUSS |
2017 |
A Quick Look at Fuzzy Matching Programming Techniques Using SAS Software |
Kirk Paul Lafler,
Stephen B. Sloan |
source |
25 |
| WUSS |
2017 |
Effectively Utilizing Loops and Arrays in the DATA Step |
Arthur X. Li |
source |
17 |
| WUSS |
2017 |
Fifteen Functions to Supercharge Your SAS Code |
Joshua M. Horstman |
source |
8 |
| WUSS |
2017 |
Many Means to a Mean |
Shannon Pileggi |
source |
9 |
| WUSS |
2017 |
PROC TABULATE Made Easy |
Yunin Ludena,
Elizabeth Guerrero Angel |
source |
15 |
| WUSS |
2017 |
Text Wrapping with Indentation for RTF Reports |
Abhinav Srivastva |
source |
6 |
| PharmaSUG CN |
2017 |
Effective Statistical Programming Preparation and Support for CFDA Self-Inspections and Onsite Inspections |
Peng Wan |
|
5 |
| PharmaSUG |
2017 |
A Macro to carry values through observations forwards or backwards over missing or 'null' values within a BY group or a SAS data set. |
Timothy J. Harrington |
source |
9 |
| PharmaSUG |
2017 |
ADQRS: Basic Principles for Building Questionnaire, Rating and Scale Analysis Datasets |
Nancy Brucken,
Karin LaPann |
source |
6 |
| PharmaSUG |
2017 |
Beyond IF THEN ELSE: Techniques for Conditional Execution of SAS Code |
Joshua M. Horstman |
source |
10 |
| PharmaSUG |
2017 |
Clinical Trials Data: It-s a Scary World Out There! or Code that Helps You Sleep at Night |
Scott Horton |
source |
10 |
| PharmaSUG |
2017 |
Common Programming Errors in CDISC data |
Sergiy Sirichenko |
source |
9 |
| PharmaSUG |
2017 |
Data Integrity: One step before SDTM |
Pavan Kathula,
Sonal Torawane |
source |
9 |
| PharmaSUG |
2017 |
Five Ways to Create Macro Variables: A Short Introduction to the Macro Language |
Arthur L. Carpenter |
source |
12 |
| PharmaSUG |
2017 |
Good Data Validation Practice |
Sergiy Sirichenko,
Max Kanevsky |
source |
10 |
| PharmaSUG |
2017 |
Implementing Patient Report Outcome data in clinical trials analysis |
Qi Wang |
source |
7 |
| PharmaSUG |
2017 |
Leveraging Study Data Reviewer-s Guide (SDRG) in Building FDA-s Confidence in Sponsor-s Submitted Datasets |
Xiangchen (Bob) Cui,
Min Chen,
Letan (Cleo) Lin |
source |
10 |
| PharmaSUG |
2017 |
Multiple Imputation: A Statistical Programming Story |
Christopher J. Smith,
Scott Kosten |
source |
16 |
| PharmaSUG |
2017 |
My bag of SAS lifehacks |
Dmytro Hasan |
source |
10 |
| SGF |
2017 |
A Moment-Matching Approach for Generating Synthetic Data in SAS |
Brittany Bogle,
Jared Erickson |
source |
17 |
| SGF |
2017 |
A Survey of Some of the Most Useful SAS Functions |
Ronald P. Cody |
source |
16 |
| SGF |
2017 |
Beyond IF THEN ELSE: Techniques for Conditional Execution of SAS Code |
Joshua M. Horstman |
source |
10 |
| SGF |
2017 |
Circular Metadata Group Membership Can Make Us Dizzy! |
Karen Hinkson,
Greg Lehner |
source |
22 |
| SGF |
2017 |
Data Can Be Beautiful: Crafting a Compelling Story with SAS Visual Analytics |
Cheryl L. Coyle,
Mark Malek,
Chelsea Mayse,
Vaidehi Patil,
Sierra Shell |
source |
20 |
| SGF |
2017 |
DS2 with Both Hands on the Wheel |
Peter W. Eberhardt,
Xue Yao |
source
zip |
32 |
| SGF |
2017 |
Examining Higher Education Performance Metrics with SAS Enterprise Miner and SAS Visual Analytics |
Taylor Blaetz,
Tuesdi Helbig,
Gina Huff,
Matt T. Bogard |
source |
15 |
| SGF |
2017 |
Exploration of Information Technology-Related Barriers Affecting Rural Primary Care Clinics |
Ipe Simon Paramel,
Ankita Srivastava,
Onkar Jadhav,
Jennifer Briggs,
Georgina Ankrah |
source |
11 |
| SGF |
2017 |
Five Ways to Create Macro Variables: A Short Introduction to the Macro Language |
Arthur L. Carpenter |
source
zip |
12 |
| SGF |
2017 |
Go Ahead and _BREAK_-down: Advanced COMPUTE Block Examples |
Cynthia L. Zender |
source
zip |
30 |
| SGF |
2017 |
Investigating Big-Data Crime Scenes |
Theresa H. Ngo |
source |
10 |
| SGF |
2017 |
It All Started with a Mouse: Storytelling with SAS Visual Analytics |
Tuesdi Helbig |
source |
16 |
| SGF |
2017 |
J2SP: An Investigation in Social Factors That Might Influence National GDP |
John Eacott,
Parselvan Aravazhi,
Sid Grover,
Jayant Sharma |
source |
16 |
| SGF |
2017 |
Leads and Lags: Static and Dynamic Queues in the SAS DATA STEP, 2nd ed. |
Mark Keintz |
source |
15 |
| SGF |
2017 |
SAS Macros for Binning Predictors with a Binary Target |
Bruce S. Lund |
source |
13 |
| SGF |
2017 |
So You Think You Can Combine Data Sets? |
Christopher J. Bost |
source |
23 |
| SGF |
2017 |
Tables and Graphics That Will FREQ You Out |
Kathryn McLawhorn |
source |
21 |
| SGF |
2017 |
Tales from the Help Desk: Solutions to Common DATA Step Tasks |
Bruce F. Gilsen |
source |
19 |
| SGF |
2017 |
Time Series Analysis and Forecasting in SAS University Edition |
Christopher Battiston |
source |
10 |
| SCSUG |
2016 |
Beyond IF THEN ELSE: Techniques for Conditional Execution of SAS Code |
Joshua M. Horstman |
source |
6 |
| SCSUG |
2016 |
Time Saver for All: A Macro Toolbox |
Philip Jou |
source |
20 |
| SCSUG |
2016 |
Using INFILE and INPUT Statements to Introduce External Data into the SAS System |
Andrew T. Kuligowski |
source |
18 |
| SESUG |
2016 |
An Easy Route to a Missing Data Report with ODS + PROC FREQ + A Data Step |
Michael S. Zdeb |
source |
10 |
| SESUG |
2016 |
Analyzing non-normal data with categorical response variables |
Niloofar Ramezani,
Allameh Tabataba'i |
source |
9 |
| SESUG |
2016 |
Array Programming Basics |
John J. Cohen |
source |
11 |
| SESUG |
2016 |
CMISS the SAS Function You May Have Been MISSING |
Mira Shapiro |
source |
6 |
| SESUG |
2016 |
Know your Interest Rate |
Anirban Chakraborty,
Goutam Chakraborty |
source |
16 |
| SESUG |
2016 |
Leads and Lags: Static and Dynamic Queues in the SAS DATA STEP |
Mark Keintz |
source |
15 |
| SESUG |
2016 |
Missing Data and Complex Sample Surveys Using SAS: The Impact of Listwise Deletion vs. Multiple Imputation on Point and Interval Estimates when Data are MCAR and MAR |
Anh P. Kellermann,
DeAnn Trevathan,
Jeffrey D. Kromrey |
source |
12 |
| SESUG |
2016 |
PROC DOC III: Self-generating Codebooks Using SAS |
Louise S. Hadden |
source |
25 |
| SESUG |
2016 |
Sankey Diagram with Incomplete Data - From a Medical Research Perspective |
Sankey Diagram |
source |
14 |
| SESUG |
2016 |
Some _FILE_ Magic |
Michael S. Zdeb |
source |
7 |
| SESUG |
2016 |
Tales from the Help Desk 7: Solutions to Common SAS Tasks |
Bruce F. Gilsen |
source |
11 |
| SESUG |
2016 |
The Power of PROC FORMAT |
Jonas V. Bilenas |
source |
10 |
| SESUG |
2016 |
Using CALL VNAME to Populate Missing Data from a Default Values Lookup Dataset |
Raghav Adimulam |
source |
6 |
| SESUG |
2016 |
Using PROC EXPAND to Easily Manipulate Longitudinal and Panel Data |
Matthew J. Hoolsema,
Carnegie Mellon |
source |
6 |
| SESUG |
2016 |
What Do You Mean My CSV Doesn't Match My SAS Dataset? |
Patricia Guldin |
source |
10 |
| PHUSE EU |
2016 |
Standard Methods for Analysis and Reporting of VAS or NRS Derived Pain Relief Response Scores |
James R. Johnson |
source
presentation source |
14 |
| MWSUG |
2016 |
A Quick Macro to Replace Missing Values with NULL for Numeric Fields within a CSV File |
John Schmitz |
|
4 |
| MWSUG |
2016 |
Beyond IF THEN ELSE: Techniques for Conditional Execution of SAS Code |
Joshua M. Horstman |
|
6 |
| MWSUG |
2016 |
Defensive Coding by Example: Kick the Tires, Pump the Brakes, Check Your Blind Spots, and Merge Ahead! |
Nancy Brucken,
Donna E. Levy |
|
16 |
| MWSUG |
2016 |
I Have a Secret: How Can I Hide Small Numbers from Public View? |
Fred Edora |
|
6 |
| MWSUG |
2016 |
Leads and Lags: Static and Dynamic Queues in the SAS DATA STEP |
Mark Keintz |
|
15 |
| MWSUG |
2016 |
Pre-Data Checks for SDTM Development |
Abhinav Srivastva |
|
10 |
| MWSUG |
2016 |
Time is of Essence: The power of "MISS" is "NOT being missed"! |
Gowri Madhavan,
Brittney Delev |
|
11 |
| MWSUG |
2016 |
Using Animation to Make Statistical Graphics Come to Life |
Jesse M. Pratt |
|
10 |
| PharmaSUG CN |
2016 |
Why not Picture Format? |
Gaoyang Li |
|
16 |
| WUSS |
2016 |
Best Practices in Machine Learning Applications |
Brett Wujek |
source |
23 |
| WUSS |
2016 |
Beyond IF THEN ELSE: Techniques for Conditional Execution of SAS Code |
Joshua M. Horstman |
source |
6 |
| WUSS |
2016 |
Errors, Warnings, and Notes (Oh My) A Practical Guide to Debugging SAS Programs |
Lora D. Delwiche,
Susan J. Slaughter |
source |
16 |
| WUSS |
2016 |
Getting it Done with PROC TABULATE |
Michael Williams |
source |
14 |
| WUSS |
2016 |
Good Programming Practice [GPP] in SAS & Clinical Trials |
Phaneendhar Reddy Vanam,
Srinivas Vanam,
Manvitha Yennam |
source |
10 |
| WUSS |
2016 |
How to analyze correlated and longitudinal data? |
Niloofar Ramezani |
source |
8 |
| WUSS |
2016 |
How to Use PROC Tabulate - A Hands-on Workshop |
Natasha Chapman |
source |
12 |
| WUSS |
2016 |
MIghty PROC MI to the rescue! |
Cynthia Alvarez |
source |
10 |
| WUSS |
2016 |
The REPORT Procedure: A Primer for the Compute Block |
Jane Eslinger |
source |
23 |
| WUSS |
2016 |
Ushering SAS Emergency Medicine into the 21st Century: Toward Exception Handling Objectives, Actions, Outcomes, and Comms |
Troy Martin Hughes |
source |
23 |
| WUSS |
2016 |
What just happened? A visual tool for highlighting differences between two data sets. |
Steve Cavill |
source |
18 |
| PharmaSUG |
2016 |
A Data Preparation Primer: Getting Your Data Ready for Submission |
Janet E. Stuelpner |
source |
3 |
| PharmaSUG |
2016 |
Best Practices: Subset Without Getting Upset |
Mary F. O. Rosenbloom,
Kirk Paul Lafler |
source |
17 |
| PharmaSUG |
2016 |
Beyond IF THEN ELSE: Techniques for Conditional Execution of SAS Code |
Joshua M. Horstman |
source |
6 |
| PharmaSUG |
2016 |
Compilation of Errors, Warnings and Notes! |
Anusuiya Ghanghas,
Houde Zhang,
Rajinder Kumar |
source |
7 |
| PharmaSUG |
2016 |
I Object: SAS Does Objects with DS2 |
Peter W. Eberhardt,
Xue Yao |
source |
27 |
| PharmaSUG |
2016 |
Quick and Efficient Way to Check the Transferred Data |
Divyaja Padamati |
source |
6 |
| PharmaSUG |
2016 |
SAS Functions You May Have Been MISSING |
Mira Shapiro |
source |
8 |
| PharmaSUG |
2016 |
Scrambled Data - A Population PK/PD Programming Solution |
Sharmeen Reza |
source |
8 |
| SGF |
2016 |
A Survey of Some of the Most Useful SAS Functions |
Ronald P. Cody |
source |
16 |
| SGF |
2016 |
Analyzing Non-Normal Binomial and Categorical Response Variables under Varying Data Conditions |
Niloofar Ramezani |
source |
16 |
| SGF |
2016 |
Best Practices for Machine Learning Applications |
Brett Wujek,
Funda Güneş,
Patrick Hall |
source |
23 |
| SGF |
2016 |
Can You Read This into SAS for Me? Using INFILE and INPUT to Load Data into SAS |
Peter W. Eberhardt,
Audrey Yeo |
source |
20 |
| SGF |
2016 |
Case Study Using SAS Forecast Studio: Daily Taxi Data from New York City [presentation] |
Simon Sheather |
source |
47 |
| SGF |
2016 |
Cleaning Up Your SAS Log: Note Messages |
Jennifer M. Srivastava |
source |
8 |
| SGF |
2016 |
From SAS Data Management to Big Data Appliances: How SAS/ACCESS Makes Life Easier |
Magali Thesias |
source
presentation |
13 |
| SGF |
2016 |
From Sensors to Solutions: An Example of Analytics in Motion |
Milad Falahi,
Gilbert Hernandez |
source |
12 |
| SGF |
2016 |
How to STRIP Your Data: Five Go-To Steps to Assure Data Quality |
Michael Santema,
Fagen Xie |
source |
6 |
| SGF |
2016 |
Leads and Lags: Static and Dynamic Queues in the SAS DATA STEP |
Mark Keintz |
source |
15 |
| SGF |
2016 |
Mastering Data Summarization with PROC SQL |
Christianna S. Williams |
source |
20 |
| SGF |
2016 |
Missing Values: They Are NOT Nothing |
Justin Jia,
Shan Shan Lin |
source |
19 |
| SGF |
2016 |
Multiple-Group Multiple Imputation: An Empirical Comparison of Parameter Recovery in the Presence of Missing Data using SAS |
Patricia Rodríguez de Gil,
Jeffrey D. Kromrey |
source |
15 |
| SGF |
2016 |
Predicting If Mental Health Facilities Will Offer Free Treatment to Patients Who Cannot Afford It |
Ram Poudel,
Lynn Xiang |
source |
11 |
| SGF |
2016 |
Predicting Occurrence Rate of Systemic Lupus Erythematosus and Rheumatoid Arthritis in Pregnant Women |
Ravikrishna Vijaya Kumar,
Shrie Raam Sathyanarayanan |
source |
15 |
| SGF |
2016 |
PROC DS2 with Both Hands on the Wheel |
Peter W. Eberhardt,
Xue Yao |
source |
32 |
| SGF |
2016 |
Sorting a Bajillion Records: Conquering Scalability in a Big Data World |
Troy Martin Hughes |
source
presentation |
29 |
| SGF |
2016 |
Strategies for Developing and Delivering an Effective Online SAS Programming Course |
Justina M. Flavin |
source |
11 |
| SGF |
2016 |
Streaming Decisions: How SAS Puts Streaming Data to Work |
Fiona McNeill,
David R. Duling,
Steve Sparano |
source
zip |
15 |
| SGF |
2016 |
Survey Data Imputation with PROC SURVEYIMPUTE |
Pushpal K. Mukhopadhyay |
source |
26 |
| SGF |
2016 |
Using Animation to Make Statistical Graphics Come to Life |
Jesse M. Pratt |
source |
10 |
| SGF |
2016 |
You Can Bet on It: Missing Observations Are Preserved with the PRELOADFMT and COMPLETETYPES Options |
Christopher J. Boniface,
Janet Wysocki |
source |
7 |
| MWSUG |
2015 |
A Macro for Systematic Treatment of Special Values in Weight of Evidence Variable Transformation |
Chaoxian Cai |
|
9 |
| MWSUG |
2015 |
Are You Missing Out? Working with Missing Values to Make the Most of What is not There |
Arthur L. Carpenter |
|
12 |
| MWSUG |
2015 |
In Search of the LOST CARD |
Andrew T. Kuligowski |
|
9 |
| MWSUG |
2015 |
Let SAS Cleanse Your Dirty Data |
Kaushal Chaudhary,
Deanna Naomi Schreiber-Gregory |
|
13 |
| MWSUG |
2015 |
Multiple Imputation for Arbitrary Missing Data: SAS and R |
Kaushal Chaudhary,
Deanna Naomi Schreiber-Gregory |
|
7 |
| MWSUG |
2015 |
Predictive Modeling Using Artificial Neural Networks in SAS Enterprise Miner |
Kechen Zhao |
|
4 |
| MWSUG |
2015 |
Slice and Dice your customers easily by using SAS Clustering Procedures |
Yanping Shen |
|
9 |
| MWSUG |
2015 |
The REPORT Procedure: A Primer for the Compute Block |
Jane Eslinger |
|
23 |
| MWSUG |
2015 |
Ushering SAS Emergency Medicine into the 21st Century: Toward Exception Handling Objectives, Actions, Outcomes, and Comms |
Troy Martin Hughes |
|
23 |
| MWSUG |
2015 |
You Can't Spell 'Assume' without S.A.S. |
Mike D. Tangedal |
|
8 |
| PHUSE EU |
2015 |
Dataset-XML - A New CDISC Standard for Data Exchange |
Julie A. Maddox,
Lex Jansen |
source
presentation source |
16 |
| PHUSE EU |
2015 |
Efficient Data Reviews and Quality in Clinical Trials Using CDISC Data Standards |
Valérie Nedbal |
source |
11 |
| PHUSE EU |
2015 |
Facing Your Fear of the Unknown - Handling Missing Data |
Amanda Reilly |
source
presentation source |
7 |
| PHUSE EU |
2015 |
Programming Strategically for PK/PD Data |
Grace Lu |
source
presentation source |
8 |
| SESUG |
2015 |
29 Shades of Missing |
Darryl Putnam |
source |
5 |
| SESUG |
2015 |
A SAS Algorithm for Imputing Discrete Missing Outcomes Based on Minimum Distance |
Minimum Distance |
source |
7 |
| SESUG |
2015 |
A SAS Macro for Improved Correlation Coefficient Inference |
Stephen W. Looney |
source |
7 |
| SESUG |
2015 |
A Survey of Some of the Most Useful SAS Functions |
Ronald P. Cody |
source |
16 |
| SESUG |
2015 |
All Data Are (Most Likely) Not Created Equal: A SAS Macro to Compare Structure and Data Across Multiple Datasets |
Jason L. Salemi |
source |
13 |
| SESUG |
2015 |
An Empirical Comparison of Multiple Imputation Approaches for Treating Missing Data in Observational Studies |
Yan Wang,
Seang-Hwane Joo,
Jeffrey D. Kromrey |
source |
20 |
| SESUG |
2015 |
Automating Preliminary Data Cleaning in SAS |
Alec Zhixiao Lin |
source |
5 |
| SESUG |
2015 |
How to DoW |
Paul M. Dorfman |
source |
12 |
| SESUG |
2015 |
Prescription Opioid Use in the U.S. in 2012: Characterizing Sustained vs. Infrequent Use Using the Medical Expenditure Panel Survey |
Monika C. Lemke |
source |
16 |
| SESUG |
2015 |
Rapidly Assessing Data Completeness |
David H. Abbott |
source |
13 |
| WUSS |
2015 |
A Brief Introduction to Some Object-Oriented Programming (OOP) Concepts for SAS Programmers |
Andra Northup |
source |
19 |
| WUSS |
2015 |
A Risk Factor Coding Custom Report Created With ODS Report Writing Interface With Recent Literature and Peer Review |
Robert Springborn |
source |
13 |
| WUSS |
2015 |
Are You Missing Out? Working with Missing Values to Make the Most of What is not There |
Arthur L. Carpenter |
source |
12 |
| WUSS |
2015 |
Chi-Square and T-Tests Using SAS: Performance and Interpretation |
Jennifer L. Waller,
Maribeth H. Johnson |
source |
12 |
| WUSS |
2015 |
Leads and Lags: Static and Dynamic Queues in the SAS DATA STEP |
Mark Keintz |
source |
15 |
| WUSS |
2015 |
Maintaining Formats when Exporting Data from SAS into Microsoft Excel |
Nathaniel Derby,
Colleen E. McGahan |
source |
11 |
| WUSS |
2015 |
ODS Output Data Sets That Work for You |
Stuart D. Long,
Edward Heaton,
Daniel R. Scharf |
source |
29 |
| WUSS |
2015 |
Pattern-Mixture Random-Effects Models for Non-Ignorable Missing Data in Longitudinal Studies |
Shelley A. Blozis,
Ricardo Villarreal |
source |
3 |
| WUSS |
2015 |
S-M-U (Set, Merge, and Update) Revisited |
Andrew T. Kuligowski |
source |
13 |
| WUSS |
2015 |
You Say 'Day-ta', I Say 'Dat-a' - Measuring Coding Differences, Considering Integrity and Transferring Data Quickly |
Mary Federico Katz,
Jason R. Beene |
source |
13 |
| PharmaSUG CN |
2015 |
Approaches to Missing Data in the Analysis of SpondyloArthritis International Society (ASAS 20) Response and the Creation of the Related CDISC Compliant Analysis Datasets |
Christine Joy Dureza |
|
12 |
| PharmaSUG CN |
2015 |
Clinical trials optimization: Monte Carlo Simulation modeling and SAS applications |
Ye Meng |
|
5 |
| PharmaSUG CN |
2015 |
Effectively Utilizing Loops and Arrays in the DATA Step |
Arthur X. Li |
|
17 |
| PharmaSUG CN |
2015 |
Reading and Resolving OpenCIDSC Message [presentation] |
Yu Pang |
|
23 |
| PharmaSUG CN |
2015 |
SAS Tools for Working with Dataset-XML files |
Lex Jansen |
|
18 |
| PharmaSUG CN |
2015 |
Why Statistical Analysis Plan (SAP) should be comprehensive. |
Riddhi Merchant,
Ranjith Prayankotveettil |
|
6 |
| PharmaSUG |
2015 |
A Toolkit to Create a Dynamic Excel Format Metadata to Assist SDTM Mapping Process |
Huei-Ling Chen,
Helen Wang |
source |
11 |
| PharmaSUG |
2015 |
Defensive Coding by Example: Kick the Tires, Pump the Breaks, Check Your Blind Spots, and Merge Ahead! |
Nancy Brucken,
Donna E. Levy |
source |
16 |
| PharmaSUG |
2015 |
DS2 with Both Hands on the Wheel |
Peter W. Eberhardt,
Xue Yao |
source |
32 |
| PharmaSUG |
2015 |
Improving Data Quality - Missing Data Can Be Your Friend! |
Julie Chen |
source |
12 |
| PharmaSUG |
2015 |
Is Your Failed Macro Due To Misjudged "Timing"? |
Arthur X. Li |
source |
22 |
| PharmaSUG |
2015 |
LOCF vs. LOV in Endpoint DTYPE Development with CDISC ADaM Standards |
Maggie Ci Jiang |
source |
6 |
| PharmaSUG |
2015 |
Looking Beneath the Surface of Sorting |
Andrew T. Kuligowski |
source |
10 |
| PharmaSUG |
2015 |
Missing Data For Repeated Measures: Single Imputation VS Multiple Imputation |
Giulia Tonini,
Simona Scartoni,
Camilla Paoli,
Andrea Nizzardo,
Angela Capriati |
source |
10 |
| PharmaSUG |
2015 |
PROC TRANSPOSE For Fun And Profit |
John J. Cohen |
source |
18 |
| PharmaSUG |
2015 |
SAS Tools for Working with Dataset-XML files |
Lex Jansen |
source |
18 |
| PharmaSUG |
2015 |
The REPORT Procedure: A Primer for the Compute Block |
Jane Eslinger |
source |
23 |
| PharmaSUG |
2015 |
Things Are Not Always What They Look Like: PROC FORMAT in Action |
Peter W. Eberhardt,
Lucheng Shao |
source |
23 |
| PharmaSUG |
2015 |
Using INFILE and INPUT Statements to Introduce External Data into SAS |
Andrew T. Kuligowski |
source |
18 |
| SGF |
2015 |
An Empirical Comparison of Multiple Imputation Approaches for Treating Missing Data in Observational Studies |
Patricia Rodríguez de Gil,
Shetay Ashford,
Chunhua Cao,
Eun Sook Kim,
Rheta Lanehart,
Reginald Lee,
Jessica Montgomery,
Yan Wang |
source |
20 |
| SGF |
2015 |
Analyzing Messy and Wide Data on a Desktop |
John P. Sall |
source |
4 |
| SGF |
2015 |
Best Practices: Subset Without Getting Upset |
Mary F. O. Rosenbloom,
Kirk Paul Lafler |
source |
17 |
| SGF |
2015 |
Can You Read This into SAS for Me? Using INFILE and INPUT to Load Data into SAS |
Peter W. Eberhardt,
Audrey Yeo |
source |
20 |
| SGF |
2015 |
Chi-Square and T-Tests Using SAS: Performance and Interpretation |
Jennifer L. Waller,
Maribeth H. Johnson |
source |
12 |
| SGF |
2015 |
Defensive Coding by Example: Kick the Tires, Pump the Breaks, Check Your Blind Spots, and Merge Ahead! |
Donna E. Levy,
Nancy Brucken |
source |
17 |
| SGF |
2015 |
Demystifying Date and Time Intervals |
Derek P. Morgan |
source |
13 |
| SGF |
2015 |
Driving SAS with Lua |
Paul Tomas |
source |
16 |
| SGF |
2015 |
DS2 with Both Hands on the Wheel |
Peter W. Eberhardt,
Xue Yao |
source |
32 |
| SGF |
2015 |
How Best to Effectively Teach the SAS Language |
Arthur X. Li |
source |
13 |
| SGF |
2015 |
I Object: SAS Does Objects with DS2 |
Peter W. Eberhardt,
Xue Yao |
source |
27 |
| SGF |
2015 |
Improving the Performance of Data Mining Models with Data Preparation Using SAS Enterprise Miner |
Ricardo Galante |
source |
21 |
| SGF |
2015 |
Imputing Missing Data using SAS |
Christopher Yim |
source |
9 |
| SGF |
2015 |
Leads and Lags: Static and Dynamic Queues in the SAS DATA STEP |
Mark Keintz |
source
zip |
14 |
| SGF |
2015 |
Member-Level Regression Using SAS Enterprise Guide and SAS Forecast Studio |
Leigh McCormack,
Prudhvidhar Perati |
source |
8 |
| SGF |
2015 |
Modelling Operational Risk Using Extreme Value Theory and Skew t-Copulas via Bayesian Inference Using SAS |
Betty Johanna Garzon Rozo |
source |
20 |
| SGF |
2015 |
Multiple Imputation Using the Fully Conditional Specification Method: A Comparison of SAS, Stata, IVEware, and R |
Patricia A. Berglund |
source |
17 |
| SGF |
2015 |
Performing Efficient Transposes on Large Teradata Tables Using SQL Explicit Pass-Through |
Tao Cheng |
source |
17 |
| SGF |
2015 |
Predicting Readmission of Diabetic Patients Using the High-Performance Support Vector Machine Algorithm of SAS Enterprise Miner 13.1 |
Hephzibah Munnangi,
Goutam Chakraborty |
source |
10 |
| SGF |
2015 |
PROC SQL for PROC SUMMARY Stalwarts |
Christianna S. Williams |
source |
19 |
| SGF |
2015 |
S-M-U (Set, Merge, and Update) Revisited |
Andrew T. Kuligowski |
source |
13 |
| SGF |
2015 |
Sankey Diagrams in SAS Visual Analytics |
Varsha Chawla,
Renato Luppi |
source |
21 |
| SGF |
2015 |
SAS Formats Top Ten |
Christianna S. Williams |
source |
18 |
| SGF |
2015 |
Some of the Most Useful SAS Functions |
Ronald P. Cody |
source |
16 |
| SGF |
2015 |
Statistical Analysis of Publicly Released Survey Data with SAS/STAT Software SURVEY Procedures |
Donna Brogan |
source |
20 |
| SGF |
2015 |
The Essentials of SAS Dates and Times |
Derek P. Morgan |
source
zip |
18 |
| SGF |
2015 |
The REPORT Procedure: A Primer for the Compute Block |
Jane Eslinger |
source |
23 |
| SGF |
2015 |
The SAS Scalable Performance Data Engine: Moving Your Data to Hadoop without Giving Up the SAS Features You Depend On |
Lisa Brown |
source |
10 |
| SGF |
2015 |
Using PROC SURVEYREG and PROC SURVEYLOGISTIC to Assess Potential Bias |
Lucy D'Agostino McGowan,
Alice Toll |
source |
8 |
| SGF |
2015 |
Using SAS Enterprise Miner to Predict Breast Cancer at an Early Stage |
Gibson Ikoro,
Beatriz de la Iglesia |
source |
9 |
| SGF |
2015 |
Want an Early Picture of the Data Quality Status of Your Analysis Data? SAS Visual Analytics Shows You How |
Gerhard Svolba |
source
zip |
19 |
| SGF |
2015 |
You Say 'Day-ta', I Say 'Dat-a' - Measuring Coding Differences, Considering Integrity and Transferring Data Quickly |
Jason R. Beene,
Mary Federico Katz |
source |
14 |
| SCSUG |
2014 |
Conditional Processing Using the Case Expression in PROC SQL |
Kirk Paul Lafler |
|
8 |
| SCSUG |
2014 |
Using Enterprise Guide for Table Joins and Sets |
Warren Schlechte,
John Barry Taylor |
|
13 |
| SESUG |
2014 |
A Comprehensive Automated Data Management System For Clinical Trials |
Heather F. Eng,
Jason A. Lyons,
Theresa M. Sax |
source |
12 |
| SESUG |
2014 |
Combining Multiple Date-Ranged Historical Data Sets with Dissimilar Date Ranges into a Single Change History Data Set |
Jim Moon |
source |
9 |
| SESUG |
2014 |
FORMATs Top Ten |
Christianna S. Williams |
source |
18 |
| SESUG |
2014 |
PROC MEANS for Disaggregating Statistics in SAS: One Input Data Set and One Output Data Set with Everything You Need |
Imelda C. Go,
Abbas S. Tavakoli |
source |
8 |
| SESUG |
2014 |
PROC SQL for PROC SUMMARY Stalwarts |
Christianna S. Williams |
source |
18 |
| SESUG |
2014 |
PROC TRANSPOSE For Fun And Profit |
John J. Cohen |
source |
18 |
| SESUG |
2014 |
The DoW-Loop |
Paul M. Dorfman,
Lessia S. Shajenko |
source |
12 |
| PHUSE EU |
2014 |
A New Era: Open Access to Clinical Trial Data [presentation] |
Guillaume Breton |
source |
21 |
| PHUSE EU |
2014 |
Considerations in Creating Transparent SDTM-Based Datasets |
Jerry Salyers,
Richard Lewis |
source
presentation source |
8 |
| PHUSE EU |
2014 |
Do You Like What You See? - Understanding Data in Magnetic Resonance Imaging Studies |
Oliver Wirtz |
source
presentation source |
7 |
| PHUSE EU |
2014 |
Multiple Imputation: Better than Single Imputation in Pain Studies |
Ashik Chowdhury |
source
presentation source |
12 |
| PHUSE EU |
2014 |
R for SAS Programmers: It's Different, but Friendly |
Friedrich Schuster |
source
presentation source |
10 |
| PHUSE EU |
2014 |
Risk based monitoring using integrated clinical development platform Authors |
Sita Rama Swamy Peddiboyina,
Shailesh Vijay Joshi |
source
presentation source |
9 |
| PHUSE EU |
2014 |
Small Sample Bayesian Factor Analysis |
Dirk Heerweghm |
source
presentation source |
18 |
| MWSUG |
2014 |
A Comprehensive Automated Data Management System For Clinical Trials |
Heather F. Eng,
Jason A. Lyons,
Theresa M. Sax |
|
12 |
| MWSUG |
2014 |
Comparing regression, propensity matching and coarsened exact matching in healthcare observational studies using SAS: An example from the Medical Expenditure Panel Survey (MEPS) |
Douglas Thompson |
|
16 |
| MWSUG |
2014 |
Creating an Easy to Use, Dynamic, Flexible Summary Table Macro with P-Values in SAS for Research Studies |
Amy Gravely,
Barbara Clothier,
Sean Nugent |
|
10 |
| MWSUG |
2014 |
Creating Code writing algorithms for producing n-lagged variables |
Matthew Bates |
|
9 |
| MWSUG |
2014 |
FORMATs Top Ten |
Christianna S. Williams |
|
18 |
| MWSUG |
2014 |
I Object: SAS Does Objects with DS2 |
Peter W. Eberhardt,
Xue Yao |
|
29 |
| MWSUG |
2014 |
Programming With CLASS: Keeping Your Options Open |
Arthur L. Carpenter |
|
15 |
| MWSUG |
2014 |
Quick Hits - My Favorite SAS Tricks |
Marje Fecht |
|
13 |
| MWSUG |
2014 |
SAS PASSTHRU to Microsoft SQL Server using ODBC |
Nina L. Werner |
|
6 |
| MWSUG |
2014 |
SAS: Data Manipulation Tools |
Audrey Yeo |
|
12 |
| MWSUG |
2014 |
Some useful SQL Procedures in SAS ? Applications in complex scenarios |
Soma Ghosh |
|
12 |
| MWSUG |
2014 |
Understanding How the DATA Step Works |
Arthur X. Li |
|
13 |
| MWSUG |
2014 |
Using the time-closest non-missing value to replace the missing value |
Yubo Gao |
|
4 |
| PharmaSUG CN |
2014 |
Building a Partnership: Constructing a global team between a US Sponsor and a Chinese CRO |
Jeanina M. Worden |
|
8 |
| PharmaSUG CN |
2014 |
Marco free automation to identify and drop variables with all values as missing |
Vamse Goutam |
|
5 |
| WUSS |
2014 |
A Survey of Some of the Most Useful SAS Functions |
Ronald P. Cody |
source |
16 |
| WUSS |
2014 |
Best Practices: Subset Without Getting Upset |
Mary F. O. Rosenbloom,
Kirk Paul Lafler |
source |
17 |
| WUSS |
2014 |
Dealing with Missing Data in Clinical Trials |
Lucheng Shao |
source |
12 |
| WUSS |
2014 |
Don't Let a Bad Date Ruin Your Day: Dealing with Invalid Dates in SAS |
Lucheng Shao |
source |
9 |
| WUSS |
2014 |
How to Use Proc Tabulate - Hands-on workshop |
Tasha Chapman |
source |
12 |
| WUSS |
2014 |
Intervals, Enhanced |
Derek P. Morgan |
source |
12 |
| WUSS |
2014 |
LEADS AND LAGS: HANDLING QUEUES IN THE SAS DATA STEP |
Mark Keintz |
source |
12 |
| WUSS |
2014 |
Macro free automation to identify and drop variables with all values as missing |
Amarnath Vijayarangan,
Vamse Goutam |
source |
5 |
| WUSS |
2014 |
Not All Equals are Created Equal: Nonstandard Statement Structures in the DATA Step |
Arthur L. Carpenter |
source |
10 |
| WUSS |
2014 |
Programming With CLASS: Keeping Your Options Open |
Arthur L. Carpenter |
source |
15 |
| WUSS |
2014 |
Quick Analysis and Visualization of Excluded and Missing Data |
Polina Kukhareva |
source |
9 |
| WUSS |
2014 |
Reanalysis of Ecological Data from the 1989 China Study Using SAS Enterprise Guide |
Thomas E. Billings |
source |
17 |
| WUSS |
2014 |
The Essentials of SAS Dates and Times |
Derek P. Morgan |
source |
17 |
| PharmaSUG |
2014 |
A User Friendly Tool to Facilitate the Data Integration Process |
Yang Wang,
Boxun Zhang |
source |
6 |
| PharmaSUG |
2014 |
Are You Missing Out? Working with Missing Values to Make the Most of What is not There |
Arthur L. Carpenter |
source |
11 |
| PharmaSUG |
2014 |
Challenges of Processing Questionnaire Data from Collection to SDTM to ADaM and Solutions Using SAS |
Karin LaPann,
Terek J. Peterson |
source |
20 |
| PharmaSUG |
2014 |
Cleaning up your SAS log: Overwritten Variable Info Messages |
Jennifer M. Srivastava |
source |
6 |
| PharmaSUG |
2014 |
Common and Comparative Incidence Indicators of Adverse Events for Well-Defined Study Pools |
John R. Gerlach |
source |
7 |
| PharmaSUG |
2014 |
Dealing with Missing Data in Clinical Trials |
Lucheng Shao |
source |
13 |
| PharmaSUG |
2014 |
Functioning at a Higher Level: Using SAS Functions to Improve Your Code |
Peter W. Eberhardt,
Lucheng Shao |
source |
12 |
| PharmaSUG |
2014 |
Handling with missing data in clinical trials for time-to-event variables |
Giulia Tonini,
Simona Scartoni,
Angela Capriati |
source |
9 |
| PharmaSUG |
2014 |
Healthcare Data Manipulation and Analytics Using SAS |
Lumin Shen,
Jane Lu |
source |
11 |
| PharmaSUG |
2014 |
I Object: SAS Does Objects with DS2 |
Peter W. Eberhardt,
Xue Yao |
source |
29 |
| PharmaSUG |
2014 |
Programming With CLASS: Keeping Your Options Open |
Arthur L. Carpenter |
source |
15 |
| PharmaSUG |
2014 |
QC made easy using macros |
Prashanthi Selvakumar |
source |
7 |
| PharmaSUG |
2014 |
Times Can Be Tough: Taming DATE, TIME and DATETIME Variables |
Sajeet Pavate |
source |
6 |
| PharmaSUG |
2014 |
Understanding and Applying the Logic of the DOW-Loop |
Arthur X. Li |
source |
12 |
| SGF |
2014 |
A Survey of Some of the Most Useful SAS Functions |
Ronald P. Cody |
source |
16 |
| SGF |
2014 |
Big Data/Metadata Governance |
Sigurd W. Hermansen |
source |
9 |
| SGF |
2014 |
Challenges of Processing Questionnaire Data from Collection to SDTM to ADaM and Solutions Using SAS |
Karin LaPann,
Terek J. Peterson |
source |
19 |
| SGF |
2014 |
Combining Type-III Analyses from Multiple Imputations |
Binhuan Wang,
Yixin Fang,
Man Jin |
source |
6 |
| SGF |
2014 |
Comparison of Five Analytic Techniques for Two-Group, Pre-Post Repeated Measures Designs Using SAS |
J. Madison Hyer,
Jennifer L. Waller |
source |
8 |
| SGF |
2014 |
Data Cleaning: Longitudinal Study Cross-Visit Checks |
Lauren Parlett |
source |
7 |
| SGF |
2014 |
Data Coarsening and Data Swapping Algorithms |
Tom Krenzke,
Katie A. Hubbell,
Mamadou Diallo,
Amita Gopinath,
Sixia Chen |
source |
19 |
| SGF |
2014 |
Effectively Utilizing Loops and Arrays in the DATA Step |
Arthur X. Li |
source |
17 |
| SGF |
2014 |
Errors, Warnings, and Notes (Oh My) A Practical Guide to Debugging SAS Programs |
Lora D. Delwiche,
Susan J. Slaughter |
source |
15 |
| SGF |
2014 |
Exporting Formulas to Microsoft Excel Using the ODS ExcelXP Tagset |
Joseph Skopic |
source |
4 |
| SGF |
2014 |
Handling Missing Data Using SAS Enterprise Guide |
Elena Shtern,
Matt Hall |
source |
21 |
| SGF |
2014 |
How to Separate Regular Prices from Promotional Prices? |
Michael J. Leonard,
Michele A. Trovero |
source |
7 |
| SGF |
2014 |
I Object: SAS Does Objects with DS2 |
Peter W. Eberhardt,
Xue Yao |
source |
27 |
| SGF |
2014 |
Internet Gambling Behavioral Markers: Using the Power of SAS Enterprise Miner 12.1 to Predict High-Risk Internet Gamblers |
Sai Vijay Kishore Movva,
Vandana Reddy,
Goutam Chakraborty |
source |
11 |
| SGF |
2014 |
Navigate the SAS Log - GPS Style |
Ethan Miller,
Rebecca Ottesen |
source |
11 |
| SGF |
2014 |
PROC TABULATE: Extending This Powerful Tool Beyond Its Limitations |
Justin Jia,
Amanda Lin |
source |
11 |
| SGF |
2014 |
Programming With CLASS: Keeping Your Options Open |
Arthur L. Carpenter |
source |
15 |
| SGF |
2014 |
Quick Hits - My Favorite SAS Tricks |
Marje Fecht |
source |
13 |
| SGF |
2014 |
SAS Enterprise Guide 5.1: A Powerful Environment for Programmers, Too! |
Marje Fecht,
Rupinder Dhillon |
source |
32 |
| SGF |
2014 |
SAS XML Programming Techniques |
Christopher W. Schacherer |
source |
39 |
| SGF |
2014 |
The Power of PROC FORMAT |
Jonas V. Bilenas |
source |
8 |
| SGF |
2014 |
The Role of Customer Response Models in Customer Solicitation Center's Direct Marketing Campaign |
Arun Mandapaka,
Amit Kushwah,
Goutam Chakraborty |
source |
12 |
| SGF |
2014 |
Understanding and Applying the Logic of the DOW-Loop |
Arthur X. Li |
source |
12 |
| SGF |
2014 |
Using SAS Enterprise Miner to Predict the Injury Risk Involved in Car Accidents |
Prateek Khare,
Vandana Reddy,
Goutam Chakraborty |
source |
7 |
| SGF |
2014 |
Using SAS to Examine Internal Consistency and to Develop Community Engagement Scores |
Renee Gennarelli,
Melody S. Goodman |
source |
10 |
| SGF |
2014 |
What You're Missing About Missing Values |
Christopher J. Bost |
source |
11 |
| WUSS |
2013 |
Factor Analysis: Quit being a whiny baby and learn it with SAS Enterprise Guide |
AnnMaria De Mars |
source |
9 |
| WUSS |
2013 |
Flexible Code: How Not to Get Stuck in a "Maze of twisty little passages, all alike" |
Paul D. Hamilton |
source |
6 |
| WUSS |
2013 |
Navigate the SAS Log - GPS Style |
Rebecca Ottesen,
Ethan Miller |
source |
11 |
| WUSS |
2013 |
Telling Stories with Your Data - Graphs, Tables and Basic, Basic Statistics with SAS Enterprise Guide |
AnnMaria De Mars |
source |
19 |
| WUSS |
2013 |
The Many Ways to Effectively Utilize Array Processing |
Arthur X. Li |
source |
14 |
| WUSS |
2013 |
Tools for Imputing Missing Data |
Taylor Lewis |
source |
16 |
| SCSUG |
2013 |
Big Data, Fast Processing Speeds |
Gary Ciampa |
source |
16 |
| SCSUG |
2013 |
The Good, The Bad, and The Ugly |
Toby Dunn,
Kirk Paul Lafler |
source |
12 |
| SCSUG |
2013 |
Using PROC STANDARD and PROC SCORE to impute missing multivariate values |
Paul Montagna |
source |
8 |
| SESUG |
2013 |
A Row Is a Row Is a Row, or Is It? A Hands-On Guide to Transposing Data |
Christianna S. Williams |
source |
17 |
| SESUG |
2013 |
Chi-Square and T-Tests Using SAS: Performance and Interpretation |
Jennifer L. Waller,
Maribeth H. Johnson |
source |
12 |
| SESUG |
2013 |
Coding For the Long Haul With Managed Metadata and Process Parameters |
Mike Molter |
source |
20 |
| SESUG |
2013 |
Comparing SAS PROC MI and IVEware Callable Software |
Bruno Vizcarra,
Amang Sukasih |
source |
11 |
| SESUG |
2013 |
Dealing with Missing Data for Credit Scoring |
Steve Fleming |
source |
7 |
| SESUG |
2013 |
HOW to DOW |
Paul M. Dorfman |
source |
12 |
| SESUG |
2013 |
Imputing Dose Levels for Adverse Events |
John R. Gerlach,
Igor Kolodezh |
source |
8 |
| SESUG |
2013 |
SAS Enterprise Guide 5.1: A Powerful Environment for Programmers, Too! |
Marje Fecht |
source |
32 |
| SESUG |
2013 |
Selecting Earliest Occurrence: Watch Your Step |
Selecting Earliest |
source |
4 |
| SESUG |
2013 |
Solving Samurai Sudoku Puzzles - A First Attempt |
John R. Gerlach |
source |
15 |
| SESUG |
2013 |
Using the 7th edition American Joint Commission on Cancer (AJCC) |
Cancer Staging Manual |
source |
13 |
| SESUG |
2013 |
What Do Your Consumer Habits Say About Your Health? Using Third-Party Data to Predict Individual Health Risk and Costs |
Satish Garla |
source |
15 |
| PHUSE EU |
2013 |
Considerations in the Submission of Holter (EG) Data in an SDTM Compliant Format |
Elsa Lozachmeur,
Sophie Arnould |
source
presentation source |
7 |
| PHUSE EU |
2013 |
EXACT - A Metadata Driven System for Creating Standard Mockups and Data Displays [presentation] |
Kent Letourneau |
source |
55 |
| PHUSE EU |
2013 |
Proc Import Diagnostic Macro |
Shan Lee |
source
presentation source |
8 |
| MWSUG |
2013 |
A Better Way to Flip (Transpose) a SAS Data Set |
Arthur S. Tabachneck,
Xia Ke Shan,
Robert Virgile,
Joe Whitehurst |
|
22 |
| MWSUG |
2013 |
Adventures in Path Analysis and Preparatory Analysis |
Brandy R. Sinco,
Phillip L. Chapman |
|
19 |
| MWSUG |
2013 |
Big Data, Fast Processing Speeds |
Kevin McGowan |
|
16 |
| MWSUG |
2013 |
Effectively Utilizing Loops and Arrays in the DATA Step |
Arthur X. Li |
|
17 |
| MWSUG |
2013 |
Introduction to REDCap for Clinical Data Collection |
Shannon M. Morrison |
|
17 |
| MWSUG |
2013 |
Maintaining Formats when Exporting Data from SAS into Microsoft Excel |
Nathaniel Derby,
Colleen E. McGahan |
|
11 |
| MWSUG |
2013 |
Not All Equals are Created Equal: Nonstandard Statement Structures in the DATA Step |
Arthur L. Carpenter |
|
10 |
| MWSUG |
2013 |
The Essence of DATA Step Programming |
Arthur X. Li |
|
19 |
| NESUG |
2013 |
A Survey of Some of the Most Useful SAS Functions |
Ronald P. Cody |
|
16 |
| NESUG |
2013 |
Big Data, Fast Processing Speeds |
Gary Ciampa |
presentation |
17 |
| NESUG |
2013 |
Dropping variables from a large SAS data set when all their values are missing |
Gwen Babcock |
|
13 |
| NESUG |
2013 |
Leads and Lags in SAS |
Mark Keintz |
|
11 |
| NESUG |
2013 |
Measuring Internal Consistency of Community Engagement using the ALPHA option of PROC CORR |
Renee Gennarelli,
Melody S. Goodman |
|
7 |
| NESUG |
2013 |
Pooling strategy for clinical data |
Abraham Yeh,
ShinRu Wang,
Xiaohong Zhang |
|
11 |
| NESUG |
2013 |
PROC TRANSPOSE For Fun And Profit |
John J. Cohen |
|
18 |
| NESUG |
2013 |
The Ultimate Macro to Generate Summary Tables |
Lan Hu,
Xiaoqiang Wang |
|
12 |
| NESUG |
2013 |
Use of SAS for Clinical Trial Management and Risk-Based Monitoring of Multicenter Clinical Trial Data from Electronic Data Capture Tools |
Bob Hall,
David Gagnon |
|
14 |
| NESUG |
2013 |
Using PROC TABULATE and ODS Style Options to Make Really Great Tables |
Wendi L. Wright |
|
19 |
| NESUG |
2013 |
What You're Missing About Missing Values |
Christopher J. Bost |
|
11 |
| PharmaSUG |
2013 |
Atypical Applications of the UPDATE Statement |
John Henry King |
source |
5 |
| PharmaSUG |
2013 |
Coding For the Long Haul With Managed Metadata and Process Parameters |
Mike Molter |
source |
20 |
| PharmaSUG |
2013 |
Combining Analysis Results from Multiply Imputed Categorical Data |
Bohdana Ratitch,
Ilya Lipkovich,
Michael O'Kelly |
source |
19 |
| PharmaSUG |
2013 |
Conditional Processing Using the Case Expression in PROC SQL |
Kirk Paul Lafler |
source |
8 |
| PharmaSUG |
2013 |
Effectively Utilizing Loops and Arrays in the DATA Step |
Arthur X. Li |
source |
17 |
| PharmaSUG |
2013 |
Essentials of PDV: Directing the Aim to Understanding the DATA Step! |
Arthur X. Li |
source |
12 |
| PharmaSUG |
2013 |
Imputing Dose Levels for Adverse Events |
John R. Gerlach,
Igor Kolodezh |
source |
8 |
| PharmaSUG |
2013 |
Not All Equals are Created Equal: Nonstandard Statement Structures in the DATA Step |
Arthur L. Carpenter |
source |
10 |
| PharmaSUG |
2013 |
SAS Logic Coding Made Easy - Revisit User-defined Function |
Songtao Jiang |
source |
6 |
| PharmaSUG |
2013 |
Solving Samurai Sudoku Puzzles - A First Attempt |
John R. Gerlach |
source |
15 |
| PharmaSUG |
2013 |
Validating Listing Output: A Better Way |
Hunter Vega,
James Kniffen Jr. |
source |
4 |
| SGF |
2013 |
A Better Way to Flip (Transpose) a SAS Data Set |
Arthur S. Tabachneck,
Joe Whitehurst,
Robert Virgile,
Xia Ke Shan |
source |
21 |
| SGF |
2013 |
A Flexible Method to Apply Multiple Imputation Using SAS/IML Studio |
Lisa M. Lix,
Xue Yao |
source
pdf |
9 |
| SGF |
2013 |
A Flock of C-Stats, or Efficiently Computing Multiple Statistics for Hundreds of Variables |
Bruce S. Lund,
Steven Raimi |
source |
11 |
| SGF |
2013 |
A Metadata-Driven Programming Technique Using SAS |
Xiyun Wang |
source |
14 |
| SGF |
2013 |
A Row Is a Row Is a Row, or Is It? A Hands-On Guide to Transposing Data |
Christianna S. Williams |
source |
16 |
| SGF |
2013 |
A SAS Macro for Applying Multiple Imputation to Multilevel Data |
Stephen Mistler |
source |
8 |
| SGF |
2013 |
A SAS Macro for Computing Pooled Likelihood Ratio Tests with Multiply Imputed Data |
Stephen Mistler |
source |
10 |
| SGF |
2013 |
Assessing Drug Safety with Bayesian Hierarchical Modeling Using PROC MCMC and JMP |
Richard C. Zink |
source |
13 |
| SGF |
2013 |
Big Data, Fast Processing Speeds |
Kevin McGowan |
source |
17 |
| SGF |
2013 |
Census Retires PROC COMPUTAB |
Christopher J. Boniface,
Hung Pham,
Nora Szeto |
source |
12 |
| SGF |
2013 |
Checking Out Your Dates with SAS |
Christopher J. Bost |
source |
5 |
| SGF |
2013 |
Chi-Square and T-Tests Using SAS: Performance and Interpretation |
Jennifer L. Waller,
Maribeth H. Johnson |
source |
12 |
| SGF |
2013 |
Do Not Let a Bad Date Ruin Your Day |
Lucheng Shao |
source |
9 |
| SGF |
2013 |
Errors, Warnings, and Notes (Oh My) A Practical Guide to Debugging SAS Programs |
Lora D. Delwiche,
Susan J. Slaughter |
source |
15 |
| SGF |
2013 |
Essentials of the Program Data Vector (PDV): Directing the Aim to Understanding the DATA Step |
Arthur X. Li |
source |
12 |
| SGF |
2013 |
From SDTM to ADaM |
Bob Lan,
Regan Li,
Sai Ma,
Suwen Li |
source
pdf |
7 |
| SGF |
2013 |
Horizontal Data Sorting and Insightful Reporting: A Useful SAS Technique |
Amanda Lin,
Justin Jia |
source |
14 |
| SGF |
2013 |
Maintaining Formats when Exporting Data from SAS into Microsoft Excel |
Colleen E. McGahan,
Nathaniel Derby |
source |
11 |
| SGF |
2013 |
Maximizing the Power of Hash Tables |
David J. Corliss |
source |
8 |
| SGF |
2013 |
Missing No More: Using the MCMC Procedure to Model Missing Data |
Fang K. Chen |
source |
23 |
| SGF |
2013 |
Not Dividing by Zero: The Last of the Low-Hanging Efficiency Fruit |
Bruce F. Gilsen |
source |
4 |
| SGF |
2013 |
Quick Hits - My Favorite SAS Tricks |
Marje Fecht |
source |
13 |
| SGF |
2013 |
Reading an Excel Spreadsheet with Cells Containing Line Endings |
Larry Hoyle |
source |
6 |
| SGF |
2013 |
SAS Data Management Techniques: Cleaning and Transforming Data for Delivery of Analytic Data Sets |
Christopher W. Schacherer |
source |
38 |
| SGF |
2013 |
SAS Enterprise Guide 5.1: A Powerful Environment for Programmers, Too! |
Marje Fecht,
Rupinder Dhillon |
source |
32 |
| SGF |
2013 |
The Magnificent DO |
Paul M. Dorfman |
source |
15 |
| SGF |
2013 |
The SAS Programmer's Guide to XML and Web Services |
Christopher W. Schacherer |
source |
32 |
| SGF |
2013 |
The Value of Neighborhood Information in Prospect Selection Models: Investigating the Optimal Level of Granularity |
Dirk Van den Poel,
Philippe Baecke |
source |
15 |
| SGF |
2013 |
Tips for Generating Percentages Using the SAS TABULATE Procedure |
Kathryn McLawhorn |
source |
13 |
| SGF |
2013 |
Weighted Sequential Hot Deck Imputation: SAS Macro vs. the SUDAAN PROC HOTDECK |
David Izrael,
Michael P. Battaglia |
source
pdf |
8 |
| SGF |
2013 |
What Do Your Consumer Habits Say About Your Health? Using Third-Party Data to Predict Individual Health Risk and Costs |
Albert Hopping,
Rick Monaco,
Sarah Rittman,
Satish Garla |
source |
15 |
| SGF |
2013 |
What Score Should Johnny Get? Missing_Items SAS Macro for Analyzing Missing Item Responses on Summative Scales |
Jeffrey D. Kromrey,
Patricia Rodríguez de Gil |
source
pdf |
12 |
| NESUG |
2012 |
A Survey of Some of the Most Useful SAS Functions |
Ronald P. Cody |
|
16 |
| NESUG |
2012 |
An Animated Guide: Regression Trees in JMP and Enterprise Miner |
Russell M. Lavery |
|
32 |
| NESUG |
2012 |
Calculating Questionnaire Scores Made Easy in SAS |
Qin Lin |
|
9 |
| NESUG |
2012 |
Checking Out Your Dates with SAS |
Christopher J. Bost |
|
6 |
| NESUG |
2012 |
Loop-Do-Loop Around Arrays |
Wendi L. Wright |
|
17 |
| NESUG |
2012 |
Managing SAS Dates with Irregular Granularity |
Howard L. Schreier |
|
10 |
| NESUG |
2012 |
Not Dividing by Zero: Last of the Low-Hanging Efficiency Fruit |
Bruce F. Gilsen |
|
4 |
| NESUG |
2012 |
SAS and R Working Together |
Matthew Cohen |
|
9 |
| NESUG |
2012 |
Some _FILE_ Magic |
Michael S. Zdeb |
|
7 |
| NESUG |
2012 |
Using PROC TABULATE and ODS Style Options to Make Really Great Tables |
Wendi L. Wright |
|
19 |
| NESUG |
2012 |
Yes, No, Maybe So: Tips and Tricks for Using 0/1 Binary Variables |
Laurie Hamilton |
|
6 |
| SCSUG |
2012 |
Dirty Data? Clean it up with SAS. |
Gabriela Cantu |
source |
11 |
| SCSUG |
2012 |
SAS Data Management Techniques: Cleaning and Transforming Data for Delivery of Analytic Data Sets |
Christopher W. Schacherer |
source |
38 |
| SCSUG |
2012 |
The Good, The Bad, and The Ugly |
Toby Dunn,
Kirk Paul Lafler |
source |
12 |
| SCSUG |
2012 |
The SAS Programmer's Guide to XML and Web Services |
Christopher W. Schacherer |
source |
32 |
| PHUSE EU |
2012 |
Interpreting ADaM Standards with OpenCDISC |
Trupti Bal,
Madhura Paranjape |
source
presentation source |
8 |
| PHUSE EU |
2012 |
Macro to Replace Missing Values |
Mokhfur Alam Chowdhury,
Kamrunnisa Runu |
source
poster source |
3 |
| PHUSE EU |
2012 |
The Logic and Logistics of Logistic Regression, Including New Features in SAS 9.2 |
Lawrence Rasouliyan |
source
presentation source |
16 |
| SESUG |
2012 |
A Standard SAS Program for Corroborating OpenCDISC Error Messages |
John R. Gerlach |
source |
11 |
| SESUG |
2012 |
An Introduction to the Clinical Standards Toolkit |
Mike Molter |
source |
13 |
| SESUG |
2012 |
Do You Have Too Much Class? |
Janet Willis |
source |
6 |
| SESUG |
2012 |
How to Perform and Interpret Chi-Square and T-Tests |
Jennifer L. Waller |
source |
14 |
| SESUG |
2012 |
Linking Medical Records to Medics in Cyberspace |
Sigurd W. Hermansen |
source |
9 |
| SESUG |
2012 |
Mastering the Basics: Preventing Problems by Understanding How SAS Works |
Imelda C. Go |
source |
14 |
| SESUG |
2012 |
Multiple Imputation for Ordinal Variables: A Comparison of SUDAAN's PROC IMPUTE vs. PROC MI |
Multiple Imputation |
source |
12 |
| SESUG |
2012 |
ODS Output Data Sets That Work for You |
Stuart D. Long,
Edward Heaton,
Daniel R. Scharf |
source |
29 |
| SESUG |
2012 |
Queries, Joins, and WHERE Clauses, Oh My!! Demystifying PROC SQL |
Christianna S. Williams |
source |
37 |
| SESUG |
2012 |
Random Effects Simulation for Sample Size Calculations Using SAS |
Random Effects Simulation |
source |
11 |
| SESUG |
2012 |
Review That You Can Do: A Guide for Systematic Review of Complex Data |
Lesa Caves |
source |
15 |
| SESUG |
2012 |
Through the DoW-Loop, One Statement at a Time |
Paul M. Dorfman |
source |
12 |
| SESUG |
2012 |
Using Dictionary Tables to Profile SAS Datasets |
Phillip Julian |
source |
18 |
| SESUG |
2012 |
Why Does SAS Say That? What Common DATA Step and Macro Messages Are Trying to Tell You |
Charles E. Mullin,
Kevin Russell |
source |
17 |
| MWSUG |
2012 |
An Analysis of Diabetes Risk Factors Using Data Mining Approach |
Akkarapol Sa-ngasoongsong,
Jongsawas Chongwatpol |
|
11 |
| MWSUG |
2012 |
Collapsing Levels of Predictor Variables for Logistic Regression and Weight of Evidence Coding |
Bruce S. Lund,
Steven Raimi |
|
13 |
| MWSUG |
2012 |
Copy and Paste Almost Anything |
Arthur S. Tabachneck,
Randy Herbison,
John Henry King,
Richard A. DeVenezia,
Nathaniel Derby,
Ben Powell |
|
17 |
| MWSUG |
2012 |
Customer Sentiment Imputation Using Hash Tables |
David J. Corliss |
|
8 |
| MWSUG |
2012 |
Effectively Utilizing Loops and Arrays in the DATA Step |
Arthur X. Li |
|
17 |
| MWSUG |
2012 |
Getting the Code Right the First Time |
Michael C. Frick |
|
11 |
| MWSUG |
2012 |
Programming With CLASS: Keeping Your Options Open |
Arthur L. Carpenter |
|
15 |
| MWSUG |
2012 |
SAS Data Management Techniques: Cleaning and Transforming Data for Delivery of Analytic Data Sets |
Christopher W. Schacherer |
|
38 |
| MWSUG |
2012 |
The Good, The Bad, and The Ugly |
Toby Dunn,
Kirk Paul Lafler |
|
12 |
| MWSUG |
2012 |
The SAS Programmer's Guide to XML and Web Services |
Christopher W. Schacherer |
|
32 |
| MWSUG |
2012 |
Using INFILE and INPUT Statements to Introduce External Data into SAS |
Andrew T. Kuligowski |
|
18 |
| WUSS |
2012 |
Assessing Interim Data Using Figures |
William Coar |
source |
6 |
| WUSS |
2012 |
Biology research data cleaning technical |
Kai Yang |
source |
12 |
| WUSS |
2012 |
Building Calendars with Proc Report |
Frank Bereznay |
source |
11 |
| WUSS |
2012 |
Habits that Help: Developing Good Programming Style |
Casey Cantrell |
source |
13 |
| WUSS |
2012 |
IF Cand IFN Functions: Alternatives to Simple DATA Step IF-THEN-ELSE, SELECT-END Code and PROC SQL CASE Statements |
Thomas E. Billings |
source |
8 |
| WUSS |
2012 |
Programming With CLASS: Keeping Your Options Open |
Arthur L. Carpenter |
source |
15 |
| WUSS |
2012 |
SAS Programming Basics: Producing Results from Your Data |
Helen D. Carey |
source |
16 |
| WUSS |
2012 |
SAS Rule-Based Codebook Generation for Exploratory Data Analysis |
Ross Bettinger |
source |
8 |
| WUSS |
2012 |
Sensitivity analysis of nonlinear mixed-effects models for longitudinal data that areincomplete |
Shelley A. Blozis |
source |
6 |
| WUSS |
2012 |
Survival Analysis with PHREG: Using MI and MIANALYZE to Accommodate Missing Data |
Arthur L. Carpenter,
Christopher F. Ake |
source |
7 |
| PharmaSUG CN |
2012 |
Systematically reordering Axis in SAS graph |
Brian Shen |
ppt |
9 |
| PharmaSUG |
2012 |
A Standard SAS Program for Corroborating OpenCDISC Error Messages |
John R. Gerlach,
Ganesh Sankaran Thangavel |
source |
12 |
| PharmaSUG |
2012 |
An Introduction to the Clinical Standards Toolkit and Clinical Data Compliance Checking |
Mike Molter |
source |
14 |
| PharmaSUG |
2012 |
Automatic Detection and Identification of Variables with All Missing Values in SDTM/ADaM Datasets for FDA Submission |
Min Chen,
Xiangchen (Bob) Cui |
source |
7 |
| PharmaSUG |
2012 |
Comparing Datasets: Using PROC COMPARE and Other Helpful Tools |
Deb Cassidy |
source |
9 |
| PharmaSUG |
2012 |
Is Your Failed Macro Due To Misjudged "Timing"? |
Arthur X. Li |
source |
22 |
| PharmaSUG |
2012 |
Multiple Techniques for Scoring Quality of Life Questionnaires |
Brandon L. Welch,
Seungshin Rhee |
source |
10 |
| PharmaSUG |
2012 |
Pattern-Mixture Models as Linear Combinations of Least Squares Means from MMRM with Delta Method Variance Estimation |
Bohdana Ratitch,
Michael O'Kelly |
source |
15 |
| PharmaSUG |
2012 |
Programming With CLASS: Keeping Your Options Open |
Arthur L. Carpenter |
source |
15 |
| PharmaSUG |
2012 |
SDTM, ADaM and define.xml with OpenCDISC |
Matthew J. Becker |
source |
14 |
| PharmaSUG |
2012 |
The power of using options COMPLETETYPES and PRELOADFMT |
Naren Surampalli |
source |
5 |
| PharmaSUG |
2012 |
The Use and Abuse of the Program Data Vector |
James R. Johnson |
source |
23 |
| PharmaSUG |
2012 |
Why Does SAS Say That? What Common DATA Step and Macro Messages Are Trying to Tell You |
Charles E. Mullin,
Kevin Russell |
source |
17 |
| SGF |
2012 |
A Simple Way to Program Power Calculations |
Robert Lew,
Hongsheng Wu |
source |
16 |
| SGF |
2012 |
A Standard SAS Program for Corroborating OpenCDISC Error Messages |
John R. Gerlach,
Ganesh Sankaran Thangavel |
source |
12 |
| SGF |
2012 |
A Survey of Some of the Most Useful SAS Functions |
Ronald P. Cody |
source |
16 |
| SGF |
2012 |
Copy and Paste Almost Anything |
Arthur S. Tabachneck,
John Henry King,
Nathaniel Derby,
Randy Herbison,
Richard A. DeVenezia,
Ben Powell |
source |
16 |
| SGF |
2012 |
Creating the Perfect Plot with PROCs EXPAND and SGPLOT: The Dynamic Duo of Time Series Display |
Andrew H. Karp |
source |
2 |
| SGF |
2012 |
Good Programming Practices: Creating Robust Programs |
Gregory S. Barnes Nelson,
Jay Zhou |
source |
16 |
| SGF |
2012 |
Handling Missing Data by Maximum Likelihood |
Paul D. Allison |
source |
21 |
| SGF |
2012 |
HOW to DOW |
Paul M. Dorfman,
Lessia S. Shajenko |
source |
12 |
| SGF |
2012 |
How to Perform and Interpret Chi-Square and T-Tests |
Jennifer L. Waller |
source |
20 |
| SGF |
2012 |
Is Your Failed Macro Due To Misjudged "Timing"? |
Arthur X. Li |
source |
22 |
| SGF |
2012 |
Kass Adjustments in Decision Trees on Binary/Interval Target |
Manoj K. Immadi |
source |
9 |
| SGF |
2012 |
Knowledge (of Your Missing Data) Is Power: Handling Missing Values in Your SAS Data Set |
Theresa Schwartz,
Rachel Zeig-Owens |
source |
8 |
| SGF |
2012 |
Let Oil and Gas Talk to You: Predicting Production Performance |
Keith R. Holdaway |
source |
12 |
| SGF |
2012 |
Queries, Joins, and WHERE Clauses, Oh My!! Demystifying PROC SQL |
Christianna S. Williams |
source |
37 |
| SGF |
2012 |
Speeding Up Discovery |
John P. Sall |
source |
13 |
| SGF |
2012 |
Tales from the Help Desk 5: Yet More Solutions for Common SAS Mistakes |
Bruce F. Gilsen |
source |
8 |
| SGF |
2012 |
The DOW Loop: A Smarter Approach to Your Existing Code |
Fuad J. Foty |
source |
12 |
| SGF |
2012 |
The FILENAME Statement: Interacting with the World Outside of SAS |
Christopher W. Schacherer |
source |
29 |
| SGF |
2012 |
The Good, The Bad, and The Ugly |
Toby Dunn,
Kirk Paul Lafler |
source |
12 |
| SGF |
2012 |
The Use and Abuse of the Program Data Vector |
James R. Johnson |
source |
23 |
| SGF |
2012 |
Using SAS GTL to Visualize Your Data when there is Too Much of it to Visualize |
Perry Watts,
Nathaniel Derby |
source |
21 |
| SGF |
2012 |
What Do You Mean, Not Everyone Is Like Me: Writing Programs for Others to Run |
Jack Hamilton |
source |
11 |
| SGF |
2012 |
Why Does SAS Say That? What Common DATA Step and Macro Messages Are Trying to Tell You |
Charles E. Mullin,
Kevin Russell |
source |
17 |
| SCSUG |
2011 |
Kass Adjustments in Decision Trees on Binary/Interval Target Variable |
Manoj Immadi |
source |
10 |
| SCSUG |
2011 |
Looking Beneath the Surface of Sorting |
Andrew T. Kuligowski |
source |
10 |
| SCSUG |
2011 |
PROC TABULATE: Getting Started |
Arthur L. Carpenter |
source |
19 |
| SCSUG |
2011 |
The FILENAME Statement: Interacting with the World Outside of SAS |
Christopher W. Schacherer |
source |
27 |
| SCSUG |
2011 |
You Can't Get There From Here If You Don't Know Where Here Is. Improving the SAS Enterprise Guide Data Characterization Task |
Patricia Hettinger |
source |
16 |
| SESUG |
2011 |
Analyst Beware: Five Dangerous Data Step Coding Traps |
David H. Abbott |
source |
9 |
| SESUG |
2011 |
Counting the Ways to Count in SAS |
Imelda C. Go |
source |
12 |
| SESUG |
2011 |
Easier than You Think: Creating Maps with SAS Enterprise Guide |
Stephanie R. Thompson |
source |
9 |
| SESUG |
2011 |
In Lockstep with the DoW-Loop |
Paul M. Dorfman,
Lessia S. Shajenko |
source |
18 |
| SESUG |
2011 |
Let's Give 'Em Something to TOC about: Transforming the Table of Contents of Your PDF File |
Bari Lawhorn |
source |
18 |
| SESUG |
2011 |
Misquoting Jane Austen in the Name of Quality Assurance |
Deborah Posner |
source |
11 |
| SESUG |
2011 |
Missing_Items: A SAS Macro for Missing Data Imputation in Summative Response Scales |
Patricia Rodríguez de Gil,
Jeffrey D. Kromrey |
source |
13 |
| SESUG |
2011 |
Proc CDISC: Implementation and Assessments |
Sheetal Nisal |
source |
6 |
| SESUG |
2011 |
PROC COMPARE - Worth Another Look! |
Christianna S. Williams |
source |
25 |
| SESUG |
2011 |
Resolving OpenCDISC Error Messages Using SAS |
John R. Gerlach |
source |
11 |
| SESUG |
2011 |
The Little Engine That Could: Using LIBNAME Engine Options to Enhance Data Transfers Between SAS and Microsoft Excel Files |
William E. Benjamin Jr. |
source |
10 |
| SESUG |
2011 |
Using SAS GTL to Visualize Your Data when there is Too Much of it to Visualize |
Perry Watts,
>>>> |
source |
22 |
| SESUG |
2011 |
Using SAS GTL to Visualize Your Data when there is Too Much of it to Visualize |
Perry Watts,
>>>> |
source |
22 |
| SESUG |
2011 |
Using SAS to Examine Aging Expectation (ERA-38) for Older Adults |
Abbas S. Tavakoli,
Julie Freelove-Charton |
source |
8 |
| SESUG |
2011 |
Using SAS to Streamline Periodic Reporting of Summary Statistics: Proc Format, Proc Freq, Proc Means,and Output Delivery System |
Berwyn Gonzalvo |
source |
2 |
| WUSS |
2011 |
Conditional Processing Using the Case Expression in PROC SQL |
Kirk Paul Lafler |
source |
8 |
| WUSS |
2011 |
Getting Freqy with PROC REPORT |
Ethan Miller |
source |
10 |
| WUSS |
2011 |
Let's Give 'Em Something to TOC about: Transforming the Table of Contents of Your PDF File |
Bari Lawhorn |
source |
18 |
| WUSS |
2011 |
Macro Tabulating Missing Values, Leveraging SAS PROC CONTENTS |
Adam Chow |
source |
9 |
| WUSS |
2011 |
PROC TABULATE: Doing More |
Arthur L. Carpenter |
source |
19 |
| WUSS |
2011 |
PROC TABULATE: Getting Started |
Arthur L. Carpenter |
source |
19 |
| WUSS |
2011 |
SAS Essentials I: SAS Functions for a Better Functioning Community |
AnnMaria De Mars |
source |
15 |
| WUSS |
2011 |
SAS Procedure Proc Mixed to Assess the Effects of School Health Policy Requirements on Change in Student Health Outcomes |
Lori Miller |
source |
9 |
| WUSS |
2011 |
Using SAS GTL to Visualize Your Data when there is Too Much of it to Visualize |
Nathaniel Derby |
source |
22 |
| PHUSE EU |
2011 |
CYA with JMP - reloaded [presentation] |
David J. Garbutt |
source |
38 |
| PHUSE EU |
2011 |
Missing Values in SAS |
Magnus Mengelbier |
source
presentation source |
11 |
| PHUSE EU |
2011 |
Using R to Validate Results of Other Programming Environments |
Nikhil Abhyankar,
Vidyagouri Prayag |
source
presentation source |
8 |
| MWSUG |
2011 |
Before Logistic Modeling - A Toolkit for Identifying and Transforming Relevant Predictors |
Steven Raimi,
Bruce S. Lund |
|
18 |
| MWSUG |
2011 |
Data Quality Review for Missing Values and Outliers |
Ying Guo,
Bradford J. Danner |
|
9 |
| MWSUG |
2011 |
Estimating Design Effects for Means, Proportions and Totals from Complex Sample Survey Data Using SAS Proc Surveymeans |
Trent D. Buskirk |
|
13 |
| MWSUG |
2011 |
In Search of the LOST CARD |
Andrew T. Kuligowski |
|
9 |
| MWSUG |
2011 |
PROC TABULATE: Getting Started |
Arthur L. Carpenter |
|
19 |
| MWSUG |
2011 |
SAS Macro: Symbols of Frustration? %Let us help! A Guide to Debugging Macros |
Kevin Payton Delaney,
Arthur L. Carpenter |
|
21 |
| MWSUG |
2011 |
The Essentials of SAS Dates and Times |
Derek P. Morgan |
|
16 |
| MWSUG |
2011 |
Using SAS GTL to Visualize Your Data when there is Too Much of it to Visualize |
Perry Watts,
Nathaniel Derby |
|
22 |
| MWSUG |
2011 |
Ways of Creating Macro Variables |
Kelley Weston |
|
8 |
| MWSUG |
2011 |
What's in a (FILE)NAME: The important role of a simple statement |
Christopher W. Schacherer |
|
27 |
| NESUG |
2011 |
An Easy Route to a Missing Data Report with ODS + PROC FREQ + A Data Step |
Michael S. Zdeb |
|
10 |
| NESUG |
2011 |
Personalizing Mailings and Driving Sales Using SAS/GRAPH |
James P. Shields |
|
10 |
| NESUG |
2011 |
PROC COMPARE - Worth Another Look! |
Christianna S. Williams |
|
26 |
| NESUG |
2011 |
SAS Does the Dirty Work, But How Do I Make It Look Pretty? |
Sarah Bobiak |
|
12 |
| NESUG |
2011 |
Tales from the Help Desk 5: Yet More Solutions for Common SAS Mistakes |
Bruce F. Gilsen |
|
8 |
| NESUG |
2011 |
The Power of PROC FORMAT |
Jonas V. Bilenas |
|
9 |
| NESUG |
2011 |
Using SAS GTL to Visualize Your Data when there is Too Much of it to Visualize |
Perry Watts,
Nathaniel Derby |
|
22 |
| PharmaSUG |
2011 |
Data Quality Review for Missing Values and Outliers |
Ying Guo,
Bradford J. Danner |
source |
8 |
| PharmaSUG |
2011 |
FAQ: Issues with Efficacy Analysis of Clinical Trial Data Using SAS |
Sandeep Sawant,
Nehan Mohan |
source |
5 |
| PharmaSUG |
2011 |
Good Programming Practices in Healthcare Creating Robust Programs |
Gregory S. Barnes Nelson,
Jay Zhou |
source |
17 |
| PharmaSUG |
2011 |
Implementation of Pattern-Mixture Models Using Standard SAS/STAT Procedures |
Bohdana Ratitch,
Michael O'Kelly |
source |
10 |
| PharmaSUG |
2011 |
Resolving OpenCDISC Error Messages Using SAS |
Virginia Redner,
John R. Gerlach |
source |
10 |
| PharmaSUG |
2011 |
The Many Ways to Effectively Utilize Array Processing |
Arthur X. Li |
source |
14 |
| PharmaSUG |
2011 |
Using SAS for Modeling and Simulation in Drug Development - A Review and Assessment of Some Available Tools |
Melvin S. Munsaka,
Michael Carniello |
source |
10 |
| PharmaSUG |
2011 |
Validating Controlled Terminology in SDTM Domains |
John R. Gerlach |
source |
12 |
| SGF |
2011 |
A SAS Macro to Consolidate Data from Multiple Cards with Varying Time Validity but Belonging to a Single Account |
Sohail Mohammad,
Goutam Chakraborty |
source |
6 |
| SGF |
2011 |
Building a Good Foundation with Functions |
Deb Cassidy |
source |
10 |
| SGF |
2011 |
Bump Hunting With SAS: A Macro Approach to Employing PRIM |
Jennifer Sniadecki |
source |
9 |
| SGF |
2011 |
Comparison of Imputation Methods to Examine Mediator Effect for Social Support of Mothers of Mentally Ill Children |
Abbas S. Tavakoli,
Kathleen Scharer,
James Hussey |
source |
7 |
| SGF |
2011 |
Conditional Classes and Pattern Recognition in SAS |
Scott Hanson |
source |
6 |
| SGF |
2011 |
Creating Reusable Programs by Using SAS Enterprise Guide Prompt Manager |
Angela M. Hall |
source |
12 |
| SGF |
2011 |
Easier than You Think: Creating Maps with SAS Enterprise Guide |
Stephanie R. Thompson |
source |
9 |
| SGF |
2011 |
Finding Your Way through the Wilderness: Moving Data from Text Files to SAS Data Files |
Charles E. Mullin |
source |
15 |
| SGF |
2011 |
Learn SAS in 50 Minutes |
Subhashree Singh,
Sovraj Singh |
source |
13 |
| SGF |
2011 |
Lets Give Em Something to TOC About: Transforming Your.pdf Files Table of Contents |
Bari Lawhorn |
source |
18 |
| SGF |
2011 |
Macro Design Automation Tool (MDAT): A Methodology for Storing Macro Design Information, Generating Documentation and Code Templates, and Automating Parameter Validation |
Richard L. Schneck,
Mindy Rodgers |
source |
12 |
| SGF |
2011 |
Making Use of Incomplete Observations in the Analysis of Structural Equation Models: The CALIS Procedures Full Information Maximum Likelihood Method in SAS/STAT 9.3 |
Yiu-Fai Yung,
Wei Zhang |
source |
20 |
| SGF |
2011 |
PROC TABULATE: A Getting Started Tutorial |
Arthur L. Carpenter |
source |
18 |
| SGF |
2011 |
PROC TABULATE: Doing More |
Arthur L. Carpenter |
source |
18 |
| SGF |
2011 |
PROC TABULATE: Getting Started |
Arthur L. Carpenter |
source |
18 |
| SGF |
2011 |
Recreating SAS Data Sets via SAS Code Generation |
Richard D. Langston |
source |
7 |
| SGF |
2011 |
Repo Your Missing Data Using PROC REPORT |
Ethan Miller |
source |
5 |
| SGF |
2011 |
SAS Web Report Studio: Exploring OLAP Data |
Sherry Patillo,
Rick Styll |
source |
25 |
| SGF |
2011 |
Social Networking and SAS: Running PROCs on Your Facebook Friends |
Susan J. Slaughter,
Chris Hemedinger |
source |
8 |
| SGF |
2011 |
Special Missing Values for Character Fields |
John F. Ladds |
source |
8 |
| SGF |
2011 |
Studying Missing Data Patterns Using a SAS Macro |
Theresa Schwartz,
Qixuan Chen,
Naihua Duan |
source |
4 |
| SGF |
2011 |
The Essence of DATA Step Programming |
Arthur X. Li |
source |
19 |
| SGF |
2011 |
The Little Engine That Could: Using LIBNAME Engine Options to Enhance Data Transfers Between SAS and Microsoft Excel Files |
William E. Benjamin Jr. |
source |
10 |
| SGF |
2011 |
The Many Ways to Effectively Utilize Array Processing |
Arthur X. Li |
source |
14 |
| SGF |
2011 |
Time Series Data Mining with SAS Enterprise Miner |
Sascha Schubert,
Taiyeong Lee |
source |
12 |
| SGF |
2011 |
Tips and Techniques for SAS Programmers |
Helen D. Carey,
Ginger Carey |
source |
19 |
| SGF |
2011 |
Using SAS Formats: So Much More than "M" = "Male" |
Peter J. Lund |
source |
14 |
| SGF |
2011 |
Using the PRELOADFMT Option with User-Defined Formats to Create Summary Tables |
Suwen Li,
Yi Hua,
Daniel Li,
Bob Lan |
source |
11 |
| SGF |
2011 |
Why .1 + .1 Might Not Equal .2 and Other Pitfalls of Floating-Point Arithmetic |
Clarke Thacher |
source |
19 |
| NESUG |
2010 |
Automatically Converting Character Variables That Store Numbers to Numeric Variables |
Christopher J. Bost |
|
13 |
| NESUG |
2010 |
HOW to DOW |
Paul M. Dorfman |
|
18 |
| NESUG |
2010 |
How to Think Through the SAS DATA Step |
H. Ian Whitlock |
|
17 |
| NESUG |
2010 |
Know What You Are Missing: How to Catalogue and Manage Missing Pieces of Historical Data |
Shankar Yaddanapudi |
|
5 |
| NESUG |
2010 |
Learn SAS in 50 Minutes |
Subhashree Singh |
|
12 |
| NESUG |
2010 |
LOG CHECKING: What to check and why? |
Sridhar R. Dodlapati,
Kiran K. Karidi,
Mahipal R. Vanam |
|
5 |
| NESUG |
2010 |
Misquoting Jane Austen in the Name of Quality Assurance |
Deborah Posner |
|
12 |
| NESUG |
2010 |
Please Don't Lag Behind LAG ! |
Anjan Matlapudi,
James D. Knapp |
|
12 |
| NESUG |
2010 |
PROC COMPARE - Worth Another Look! |
Christianna S. Williams |
|
26 |
| NESUG |
2010 |
PROC SQL: Tips and Translations |
Gail Jorgensen,
Susan P. Marcella |
|
23 |
| NESUG |
2010 |
PROC TABULATE - Building Tables With Style |
Michael G. Eberhart |
|
17 |
| NESUG |
2010 |
Quality Control: How to Analyze and Verify Financial Data |
Michelle M. Duan |
|
5 |
| NESUG |
2010 |
See All, Know All: Using PROC DOCUMENT to Produce Integrated Data Set Documentation |
Jyothi Pabbaraju |
|
17 |
| NESUG |
2010 |
Survey Analysis: Options for Missing Data |
Paul Gorrell |
|
10 |
| NESUG |
2010 |
Tips of Efficient Programming for Integrated Summary of Safety and Efficacy |
Changhong Shi,
Qing Xue |
|
6 |
| SCSUG |
2010 |
A SAS Primer for Health Care Data Analysts |
Christopher W. Schacherer |
source |
24 |
| SCSUG |
2010 |
Exploring Joins with EG |
J. Warren Schlechte |
source |
14 |
| SCSUG |
2010 |
Intro to Arrays: A Grad Student "How-To" Paper |
Elisa L. Priest |
source |
14 |
| SCSUG |
2010 |
Solving Kenken Puzzles - By Not Playing |
John R. Gerlach |
source |
10 |
| WUSS |
2010 |
Analyzing Data from Complex Samples Using PROC SURVEYFREQ in SAS 9.2 |
Kathryn Martin |
source |
7 |
| WUSS |
2010 |
Answering Your Research Questions with Descriptive Statistics |
Diana D. Suhr |
source |
7 |
| WUSS |
2010 |
Answering Your Research Questions with Inferential Statistics |
Diana D. Suhr |
source |
10 |
| WUSS |
2010 |
Creating Variables: Traps and Pitfalls |
Olena Galligan |
source |
10 |
| WUSS |
2010 |
Get a Grip on Arrays in Just 50 Minutes! |
Arthur X. Li |
source |
14 |
| WUSS |
2010 |
Habits that Help: Developing Good Programming Style |
Casey Cantrell |
source |
13 |
| WUSS |
2010 |
Introduction to Summary Tables in SAS Enterprise Guide |
Lora D. Delwiche |
source |
15 |
| WUSS |
2010 |
Practical Uses of the DOW Loop |
Richard R. Allen |
source |
10 |
| WUSS |
2010 |
PROC TABULATE: Getting Started |
Arthur L. Carpenter |
source |
18 |
| WUSS |
2010 |
Statistical Analysis - The First Steps |
Jennifer L. Waller |
source |
20 |
| WUSS |
2010 |
Strategies for Developing and Delivering an Effective Online SAS Programming Course |
Justina M. Flavin |
source |
11 |
| WUSS |
2010 |
Survival Analysis with PHREG: Using MI and MIANALYZE to Accommodate Missing Data |
Christopher F. Ake |
source |
7 |
| WUSS |
2010 |
The Building Blocks of SAS Datasets - S-M-U (Set, Merge, and Update) |
Andrew T. Kuligowski |
source |
13 |
| PHUSE EU |
2010 |
Facilitating Data Integration for Regulatory Submissions |
John R. Gerlach,
John Bowen |
source
presentation source |
8 |
| PHUSE EU |
2010 |
How to clean up dirty data in Patient reported outcomes? |
Knut Mueller |
source
presentation source |
6 |
| PHUSE EU |
2010 |
SAS/AF Frame in programmer toolkit |
Patrick Lupien |
source
presentation source |
5 |
| MWSUG |
2010 |
A SAS Primer for Health Care Data Analysts |
Christopher W. Schacherer,
Brent D. Westra |
|
24 |
| MWSUG |
2010 |
Adequacy and Information Values: Two Tools for Ranking Predictors in Generalized Linear Models |
Douglas Thompson |
|
21 |
| MWSUG |
2010 |
An Introduction to Multiple Imputation of Complex Sample Data Using SAS 9.2 |
Patricia A. Berglund |
|
12 |
| MWSUG |
2010 |
An Introduction to SQL in SAS |
Peter J. Lund |
|
21 |
| MWSUG |
2010 |
Base SAS Methods for Building Dimensional Data Models |
Christopher W. Schacherer |
|
16 |
| MWSUG |
2010 |
Comparing JMP and SAS for Validating Clinical Trials |
Sandra D. Schlotzhauer |
|
18 |
| MWSUG |
2010 |
DATA Step and PROC SQL Programming Techniques |
Kirk Paul Lafler |
|
14 |
| MWSUG |
2010 |
Errors, Warnings, and Notes (Oh My) A Practical Guide to Debugging SAS Programs |
Lora D. Delwiche,
Susan J. Slaughter |
|
15 |
| MWSUG |
2010 |
How I Learned to Type 1,388,571 wpm, Or Creating Extensive Shift Tables |
Kelley Weston |
|
7 |
| MWSUG |
2010 |
PROC TABULATE: Doing More |
Arthur L. Carpenter |
|
18 |
| MWSUG |
2010 |
PROC TABULATE: Getting Started |
Arthur L. Carpenter |
|
18 |
| MWSUG |
2010 |
Set, Match, Merge ... Don't You Love SAS |
Peter W. Eberhardt,
Ying Liu |
|
19 |
| MWSUG |
2010 |
Stuff We All Do: Mistakes We've Made That You Won't Have To |
Joe Novotny,
Michael P.D. Bramley |
|
13 |
| MWSUG |
2010 |
Survival Analysis with PHREG: Using MI and MIANALYZE to Accommodate Missing Data |
Christopher F. Ake,
Arthur L. Carpenter |
|
6 |
| MWSUG |
2010 |
Using the JMP 9 R Interfaces to Perform Bayesian Analyses: Why, How, and What |
Dave LeBlond |
|
18 |
| MWSUG |
2010 |
Ways of Creating a Macro Variable |
Kelley Weston |
|
8 |
| SESUG |
2010 |
A Taste of ADaM |
Beilei Xu |
source |
9 |
| SESUG |
2010 |
Fun With Functions |
Yogini Thakkar |
source |
10 |
| SESUG |
2010 |
PROC TABULATE: Doing More |
Arthur L. Carpenter |
source |
18 |
| SESUG |
2010 |
Set, Match, Merge ... Don't You Love SAS |
Peter W. Eberhardt,
Ying Liu |
source |
19 |
| SESUG |
2010 |
Solving Kenken Puzzles - By Not Playing |
John R. Gerlach |
source |
10 |
| SESUG |
2010 |
Statistical Analysis - The First Steps |
Jennifer L. Waller |
source |
20 |
| SESUG |
2010 |
The DOW-Loop Unrolled |
Paul M. Dorfman |
source |
15 |
| SESUG |
2010 |
Using SAS PROC CLUSTER to Determine University Benchmarking Peers |
Elayne Reiss,
Sandra J. Archer,
Robert Armacost,
Ying Sun |
source |
10 |
| SESUG |
2010 |
Using SAS to Examine Missing Data in Psychometric Research |
Abbas S. Tavakoli,
Elizabeth Register |
source |
12 |
| PharmaSUG |
2010 |
ADaM Dataset Checking Toolkit |
Huei-Ling Chen |
|
9 |
| PharmaSUG |
2010 |
Facilitating Data Integration for Regulatory Submissions |
John R. Gerlach |
|
9 |
| PharmaSUG |
2010 |
Looking for columns with all missing values |
Binoy Varghese |
|
5 |
| PharmaSUG |
2010 |
PROC TABULATE: Getting Started and Doing More |
Arthur L. Carpenter |
|
18 |
| PharmaSUG |
2010 |
PROC TABULATE: Getting Started and Doing More |
Arthur L. Carpenter |
|
18 |
| PharmaSUG |
2010 |
The Ins and Outs and Ups and Downs of Sorted Data |
Andrew T. Kuligowski |
|
9 |
| PharmaSUG |
2010 |
The Little Engine That Could: Using EXCEL LIBNAME Engine Options to Enhance Data Transfers between SAS and Microsoft Excel Files |
William E. Benjamin Jr. |
|
10 |
| PharmaSUG |
2010 |
The Most Popularly Applied Vehicles in Health Industry for Data Transfer from SAS to Microsoft Excel |
Guiping Yang |
|
23 |
| PharmaSUG |
2010 |
Validate it Again?! Get SAS to do it! |
Carol Matthews,
Ginger Lewis,
Olga Belotserkovsky |
|
13 |
| SGF |
2010 |
An Introduction to Multiple Imputation of Complex Sample Data Using SAS 9.2 |
Patricia A. Berglund |
source |
12 |
| SGF |
2010 |
Automated Data Cleaning Linking Data Manager to Study Coordinator: Error Finding by Variable and Error Fixing by Subject |
Christine T. Roman,
Emma K. Benn,
Dale C. Hesdorffer,
Anthony Marmarou,
Shlomo Shinnar |
source |
5 |
| SGF |
2010 |
Automatically Dropping Variables with Only Missing Values |
Selvaratnam Sridharma |
source |
6 |
| SGF |
2010 |
Building a Decision Tree from Decision Stumps |
Murphy Choy,
Peter L. Flom |
source |
7 |
| SGF |
2010 |
Clinical Trials Simulation: A Publicly Available, Grid-Enabled, GUI-Driven SAS System |
Peter H. Westfall,
Alex Dmitrienko,
Cynthia DeSouza,
Alin Tomoiaga |
source |
13 |
| SGF |
2010 |
dATa qWaliti 4 Analytics |
David Barkaway,
Gerhard Svolba |
source |
12 |
| SGF |
2010 |
Macros Made Easy (and More Efficient) with SAS Enterprise Guide 4.2 Prompts |
Kenneth Sucher |
source |
24 |
| SGF |
2010 |
PROC COMPARE - Worth Another Look! |
Christianna S. Williams |
source |
23 |
| SGF |
2010 |
Rapid Predictive Modeling for Customer Intelligence |
Wayne Thompson,
David R. Duling |
source |
12 |
| SGF |
2010 |
SAS Code Validation: L.E.T.O. Method |
Aaron J. Augustine |
source |
12 |
| SGF |
2010 |
SAS Presents In-Database Base Procedures in Practice |
Scott Mebust,
Robert S. Ray |
source |
25 |
| SGF |
2010 |
SAS State of Mind: A Guide to Learning SAS for the Stata User |
Sarah J. Beck |
source |
9 |
| SGF |
2010 |
SAS System Viewer Is Replaced by the SAS Universal Viewer |
Steven A. Wright |
source |
11 |
| SGF |
2010 |
SGPANEL: Telling the Story Better |
Charles D. Kincaid |
source |
10 |
| SGF |
2010 |
Stupid Human Tricks with PROC EXPAND |
David L. Cassell |
source |
6 |
| SGF |
2010 |
Tales from the Help Desk 4: Still More Solutions for Common SAS Mistakes |
Bruce F. Gilsen |
source |
8 |
| SGF |
2010 |
Taming the Herd: Wrangling Unruly Text Files in SAS |
Taylor R. Young |
source |
9 |
| SGF |
2010 |
Using Advanced Features of User-Defined Formats and Informats |
Ronald P. Cody |
source |
21 |
| SGF |
2010 |
Using SAS Formats: So Much More than "M" = "Male" |
Peter J. Lund |
source |
14 |
| SGF |
2010 |
Various Solutions to Missing Values in Repeated Sequences |
Hui Xu,
Marike Vuga |
source |
12 |
| SCSUG |
2009 |
Five Ways to Create Macro Variables: A Short Introduction to the Macro Language |
Arthur L. Carpenter |
|
12 |
| SCSUG |
2009 |
PROC REPORT - Your How-to Guide for Producing Customized Summary Tables |
Debbie Buck |
|
13 |
| SESUG |
2009 |
Customer Retention Predictive Modeling in HealthCare Insurance Industry |
Jin Su,
Kimberly Cooper,
Tina Robinson,
Brad Jordan |
source |
8 |
| SESUG |
2009 |
DATA Step versus PROC SQL Programming Techniques |
Kirk Paul Lafler |
source |
14 |
| SESUG |
2009 |
Looking Beneath the Surface of Sorting |
Looking Beneath |
source |
10 |
| SESUG |
2009 |
Multiple Imputation for Survey Data Analysis |
Yeats Ye |
source |
7 |
| SESUG |
2009 |
The Effect of Missing Data on Repeated Measures Models |
Maribeth H. Johnson |
source |
10 |
| PHUSE EU |
2009 |
Accelerate Clinical Development through Management of Data Quality [presentation] |
David Smith |
source |
30 |
| PHUSE EU |
2009 |
AN ANIMATED GUIDE: MATCHING TEST AND CONTROL SUBJECTS |
Russell M. Lavery |
source |
21 |
| PHUSE EU |
2009 |
Clinical Database acceptance: what statistical review checks are necessary to validate a database? |
Genevieve Jehl |
source |
12 |
| PHUSE EU |
2009 |
On the use of spreadsheets for statistical analysis |
Martin Gregory |
source |
7 |
| PHUSE EU |
2009 |
Standardization of data base: interaction between Biostatistics and Clinical Data Management [presentation] |
Céline Tendero |
source |
12 |
| PHUSE EU |
2009 |
Sudoku's resolution Using SAS Arrays |
Michael Gauthier |
source |
9 |
| PHUSE EU |
2009 |
Validate it Again?! Get SAS to do it! |
Carol Matthews,
Ginger Lewis |
source |
14 |
| MWSUG |
2009 |
A Comparison of Decision Tree and Logistic Regression Model |
Xianzhe Chen |
|
10 |
| MWSUG |
2009 |
Dietary Glycemic Load and Risk of Colon Cancer: A Guide to Data Management |
Svetlana Zelenskiy,
Lauren Byrne,
Cheryl L. Thompson,
Thomas C. Tucker,
David Bruckman,
Li Li |
|
16 |
| MWSUG |
2009 |
Exploring DATA Step and PROC SQL Programming Techniques |
Kirk Paul Lafler |
|
14 |
| MWSUG |
2009 |
Ranking Predictors in Logistic Regression |
Douglas Thompson |
|
13 |
| MWSUG |
2009 |
SAS Macro: Symbols of Frustration? %Let us help! A Guide to Debugging Macros |
Kevin Payton Delaney,
Arthur L. Carpenter |
|
20 |
| PNWSUG |
2009 |
Building Multiple Linear Regression Models - Food for Thought |
Colleen E. McGahan,
Mei Chong |
|
11 |
| PNWSUG |
2009 |
DATA Step versus PROC SQL Programming Techniques |
Kirk Paul Lafler |
|
14 |
| PNWSUG |
2009 |
Ecosystem food web constellation diagram using SAS/GRAPH software |
Ranjeev Kumar,
Divya Varkey |
|
7 |
| PNWSUG |
2009 |
Fifty Ways to Lose Your Data (And How to Avoid Them) |
Linda T. Jolley,
Jane Stroupe |
|
20 |
| PNWSUG |
2009 |
Five Ways to Create Macro Variables: A Short Introduction to the Macro Language |
Arthur L. Carpenter |
|
12 |
| PNWSUG |
2009 |
How to use Proc Tabulate |
Tasha Chapman |
|
12 |
| PNWSUG |
2009 |
The 7 Habits of Highly Effective SAS-ers |
David L. Cassell |
|
17 |
| PNWSUG |
2009 |
Using SAS with Oracle: Writing efficient and accurate SQL |
Tasha Chapman,
Lori Carleton |
|
6 |
| NESUG |
2009 |
Add Spices to Your Graphs Using the Annotate Facility |
Qingfeng Liang |
|
16 |
| NESUG |
2009 |
Analyzing MCBS Data with PROC SURVEYFREQ: The Maybe Cheesy but Simple Approach |
Christine E. Leonard |
|
19 |
| NESUG |
2009 |
Deciding if the EM Algorithm is the Right Choice |
Michael B. Moldoff |
|
3 |
| NESUG |
2009 |
Dropping Automatically Variables with Only Missing Values |
Selvaratnam Sridharma |
|
6 |
| NESUG |
2009 |
LOCF Method and Application in Clinical Data Analysis |
Huijuan Xu |
|
5 |
| NESUG |
2009 |
LOCF: It's more than just carrying forward |
Vikash Jain,
Niraj J. Pandya |
|
10 |
| NESUG |
2009 |
PROC SQL Tips and Techniques |
Gail Jorgensen,
Susan P. Marcella |
|
23 |
| NESUG |
2009 |
Survey Analysis: Options for Missing Data |
Paul Gorrell |
|
11 |
| NESUG |
2009 |
The Building Blocks of SAS Datasets - S-M-U (Set, Merge, and Update) |
Andrew T. Kuligowski |
|
13 |
| NESUG |
2009 |
The DOW-Loop Unrolled |
Paul M. Dorfman,
Marina Fridman |
|
18 |
| WUSS |
2009 |
Base SAS Tips, Tricks and Techniques |
Kirk Paul Lafler |
source |
14 |
| WUSS |
2009 |
Cleaning Duplicate Observations on a Chessboard of Missing Values |
Mayrita Vitvitska |
source |
6 |
| WUSS |
2009 |
Efficient Statistical Programming? - These Small Steps Make the Difference |
Keiko I. Powers |
source |
16 |
| WUSS |
2009 |
Errors, Warnings, and Notes (Oh My) A Practical Guide to Debugging SAS Programs |
Lora D. Delwiche,
Susan J. Slaughter |
source |
15 |
| WUSS |
2009 |
Five Ways to Create Macro Variables: A Short Introduction to the Macro Language |
Arthur L. Carpenter |
source |
12 |
| WUSS |
2009 |
If I Only Had a Map. Getting from Here to There: Common Approaches to Solving Data Step Logic Issues |
Kim M. Wilson |
source |
17 |
| WUSS |
2009 |
Repo Your Missing Data Using PROC REPORT |
Ethan Miller |
source |
5 |
| WUSS |
2009 |
Simple but Effective SAS-based Automation Techniques to Combine SAS Data Libraries |
Joe Perry |
source |
9 |
| WUSS |
2009 |
The Fundamentals of Data Step Programming I: The Essence of Data Step Programming and By-Group Processing |
Arthur X. Li |
source |
20 |
| WUSS |
2009 |
The Fundamentals of DATA Step Programming III: Array Processing |
Arthur X. Li |
source |
10 |
| WUSS |
2009 |
The Little Engine That Could: Using EXCEL LIBNAME Engine Options to Enhance Data Transfers between SAS and Microsoft Excel Files |
William E. Benjamin Jr. |
source |
10 |
| WUSS |
2009 |
The SURVEYFREQ Procedure in SAS 9.2: Avoiding FREQuent Mistakes When Analyzing Survey Data |
Kathryn Martin |
source |
6 |
| PharmaSUG |
2009 |
Clinical Trial Reporting Using SAS/GRAPH SG Procedures |
Susan L. Schwartz |
|
21 |
| PharmaSUG |
2009 |
Stacked Cumulative Percent Plots |
Theresa Gilligan |
|
10 |
| PharmaSUG |
2009 |
Sweet Smell of SucSAS - When Your DATA Step Does What is Intended!! |
Arthur X. Li |
|
19 |
| PharmaSUG |
2009 |
The Care and Feeding of SAS Macro Program Parameters |
Don Boudreaux |
|
9 |
| PharmaSUG |
2009 |
Translation of Drug Metabolic Enzyme and Transporter (DMET) Genetic Variants into Star Allele Notation using SAS |
Mark Farmen |
|
17 |
| SGF |
2009 |
Advanced Features of User-Defined Formats and Informats |
Ronald P. Cody |
source |
21 |
| SGF |
2009 |
Avoiding Common Traps When Accessing RDBMS Data |
Michael D. Rhoads |
source |
12 |
| SGF |
2009 |
Clinical Trial Reporting Using SAS/GRAPH SG Procedures |
Susan L. Schwartz |
source |
21 |
| SGF |
2009 |
Creating Common Information Structures Using Lists Stored in SAS Data Step Hash Objects |
Shawn Edney |
source |
19 |
| SGF |
2009 |
Fifty Ways to Lose Your Data (And How to Avoid Them) |
Linda T. Jolley,
Jane Stroupe |
source |
21 |
| SGF |
2009 |
Getting the Most Out of the SAS Survey Procedures: Repeated Replication Methods, Subpopulation Analysis, and Missing Data Options in SAS 9.2 |
Patricia A. Berglund |
source |
12 |
| SGF |
2009 |
If I Only Had a Map. Getting from Here to There: Common Approaches to Solving Data Step Logic Issues |
Kim M. Wilson |
source |
17 |
| SGF |
2009 |
LAG - the Very Powerful and Easily Misused SAS Function |
Yunchao (Susan) Tian |
source |
5 |
| SGF |
2009 |
SAS Fundamentals For Survey Data Processing |
Renato G. Villacorte |
source |
12 |
| SGF |
2009 |
The DOW-Loop Unrolled |
Paul M. Dorfman,
Koen Vyverman |
source |
18 |
| SGF |
2009 |
The Essentials of SAS Dates and Times |
Derek P. Morgan |
source |
16 |
| SGF |
2009 |
The Ins and Outs and Ups and Downs of Sorted Data |
Andrew T. Kuligowski |
source |
10 |
| SGF |
2009 |
Tips and Techniques for Analytic Web Services |
Daniel Jahn,
Bradley W. Klenz |
source |
16 |
| SGF |
2009 |
Using PROC TABULATE and ODS Style Options to Make Really Great Tables |
Wendi L. Wright |
source |
19 |
| WUSS |
2008 |
Comparing efficiency of cross validated partial least squares regression and ridge regression models in predicting spatial variability of total nitrogen concentration in Truckee River, Nevada. |
Anpalaki J. Ragavan |
source |
13 |
| WUSS |
2008 |
E Pluribus Unum: How to Obtain One Observation from Multiple Observations by Limiting, Reshaping, and Summarizing Data |
Eric P. Elkin |
source |
13 |
| WUSS |
2008 |
Fully "Function"-al: A Brief Tour of Selected SAS Numeric Functions |
Brenda Beaty |
source |
8 |
| WUSS |
2008 |
Missing by Design: Questionnaire Skip Logic |
Kathryn A. Valdes |
source |
17 |
| WUSS |
2008 |
SAS Fundamentals For Survey Data Processing |
Renato G. Villacorte |
source |
12 |
| WUSS |
2008 |
Tap Into the Power of Formats |
Nikki M. Carroll |
source |
12 |
| WUSS |
2008 |
The Essence of DATA Step Programming |
Arthur X. Li |
source |
19 |
| SCSUG |
2008 |
PROC REPORT - Your How-to Guide for Producing Customized Summary Tables |
Deborah B. Buck |
|
13 |
| SESUG |
2008 |
A macro for nearest neighbor imputation |
Lung-Chang Chien |
source |
6 |
| SESUG |
2008 |
Creating Common Information Structures Using Lists Stored in SAS Data Step Hash Objects |
Shawn Edney |
source |
18 |
| SESUG |
2008 |
MISSING VALUES: Everything You Ever Wanted to Know |
Malachy J. Foley |
source |
8 |
| SESUG |
2008 |
Non Linear Programming Of Time Series Data To Minimize Eutrophication In Truckee River, Nevada |
Anpalaki J. Ragavan |
source |
13 |
| SESUG |
2008 |
PROC REPORT - Your How-to Guide for Producing Customized Summary Tables |
Deborah B. Buck |
source |
13 |
| SESUG |
2008 |
SET, MERGE and beyond. |
Erik W. Tilanus |
source |
15 |
| SESUG |
2008 |
The Art of Debugging |
H. Ian Whitlock |
source |
20 |
| SESUG |
2008 |
The DOW-Loop Unrolled |
Paul M. Dorfman,
Lessia S. Shajenko |
source |
19 |
| PHUSE EU |
2008 |
.. but there are no ERROR messages in the LOGs |
Shafi Chowdhury |
source |
11 |
| PHUSE EU |
2008 |
Efficiency Issues in Evaluating Pharmacogenetic Data With SAS |
Volker Harm,
Stephan Lehr |
source |
10 |
| PHUSE EU |
2008 |
Missing Data in Cross-Over Trials |
Edyta Mrowka |
source |
7 |
| PHUSE EU |
2008 |
SAS macro |
Jim Groeneveld |
source |
8 |
| PHUSE EU |
2008 |
Spot the Difference |
Anja Feuerbacher,
Beate Hientzsch |
source |
13 |
| MWSUG |
2008 |
Imputation of Categorical Missing Data: A comparison of Multivariate Normal and Multinomial Methods |
Holmes Finch,
Matt Margraf |
|
12 |
| MWSUG |
2008 |
Loop-Do-Loop Around Arrays |
Wendi L. Wright |
|
17 |
| PNWSUG |
2008 |
Applied Aspects of Using Multiple Imputation in Clinical Data |
Christina Gullion |
|
7 |
| PNWSUG |
2008 |
Get Your Hands Dirty Using the FORMAT Procedure |
Marge Scerbo |
|
13 |
| NESUG |
2008 |
An Introduction to the SAS Survey Analysis PROCs |
Xiuhua Chen,
Paul Gorrell |
|
17 |
| NESUG |
2008 |
Avoiding Common Traps When Accessing RDBMS Data |
Michael D. Rhoads |
|
11 |
| NESUG |
2008 |
Tales from the Help Desk 4: Still More Solutions for Common SAS Mistakes |
Bruce F. Gilsen |
|
8 |
| NESUG |
2008 |
The DOW-Loop Unrolled |
Paul M. Dorfman |
|
18 |
| NESUG |
2008 |
Try, Try Again: Replication-Based Variance Estimation Methods for Survey Data Analysis in SAS 9.2 |
Pushpal K. Mukhopadhyay |
|
16 |
| NESUG |
2008 |
Using Formats and Other Techniques to Complete PROC REPORT Tables |
David D. Chapman |
|
12 |
| PharmaSUG |
2008 |
A SAS Macro for Single Imputation |
Shuping Zhang,
Jane X. Liao,
Xingshu Zhu |
|
8 |
| PharmaSUG |
2008 |
An Annotated Guide: Using Proc Tabulate And Proc Summary to Validate SAS Code |
Yanmei Lu,
Russell M. Lavery |
|
13 |
| PharmaSUG |
2008 |
Bayesian Data Analysis Using %WinBUGS |
Lei Zhang |
|
18 |
| PharmaSUG |
2008 |
From Soup to Nuts: Practices in Data Management for Analytical Performance |
David R. Duling |
|
25 |
| PharmaSUG |
2008 |
Innovative Techniques and Tools to Detect Data Quality Problems |
Hong Qi,
Allan Glaser |
|
8 |
| PharmaSUG |
2008 |
Labor-Saving SQL Constructs |
Brian P. Fairfield-Carter,
David W. Carr |
|
11 |
| PharmaSUG |
2008 |
Statistical Approach to Establishing Bioequivalence |
William F. McCarthy,
Nan Guo |
|
12 |
| PharmaSUG |
2008 |
The DOW-Loop Unrolled |
Paul M. Dorfman,
Lessia S. Shajenko |
|
18 |
| PharmaSUG |
2008 |
The Logic and Logistics of Logistic Regression |
Lawrence Rasouliyan,
Dave P. Miller |
|
14 |
| PharmaSUG |
2008 |
Undocumented and Hard-to-find PROC SQL Features |
Kirk Paul Lafler |
|
11 |
| PharmaSUG |
2008 |
When a Period Really is Not a Period and How to Fix It |
William C. Csont,
Howard M. Proskin |
|
4 |
| SGF |
2008 |
Advanced Features of User-Defined Formats and Informats |
Ronald P. Cody |
source |
21 |
| SGF |
2008 |
Any WAY you Want it: Getting the Right TYPEs of Observations Out of PROC SUMMARY or MEANS |
Christianna S. Williams |
source |
8 |
| SGF |
2008 |
BFF: Binary Flags Forever (Or, Boole's Paradise) |
David J. Pasta,
Eric P. Elkin |
source |
9 |
| SGF |
2008 |
Data Mining Application of Non-Linear Mixed Modeling in Water Quality Analysis |
Anpalaki J. Ragavan |
source |
14 |
| SGF |
2008 |
Easy Rolling Statistics with PROC EXPAND |
Premal P. Vora |
source |
6 |
| SGF |
2008 |
Existential Moments in Database Programming: SAS PROC SQL EXISTS and NOT EXISTS Quantifiers, and More |
Sigurd W. Hermansen,
Stanley E. Legum |
source |
6 |
| SGF |
2008 |
From Soup to Nuts: Practices in Data Management for Analytical Performance |
David R. Duling,
Howard Plemmons Jr.,
Nancy A. Rausch |
source |
24 |
| SGF |
2008 |
Get Your Hands Dirty Using the FORMAT Procedure |
Marge Scerbo |
source |
13 |
| SGF |
2008 |
How to Use SAS to Fit Multiple Logistic Regression Models |
Anpalaki J. Ragavan |
source |
13 |
| SGF |
2008 |
I Can Do That with PROC FORMAT |
Jonas V. Bilenas |
source |
12 |
| SGF |
2008 |
Identifying Potential Default Loan Applicants: A Case Study of Consumer Credit Decision for Chinese Commercial Bank |
Zhangxi Lin,
Qiwei Gan,
Binjie Luo |
source |
12 |
| SGF |
2008 |
Introduction to PROC TABULATE |
Thomas J. Winn Jr. |
source |
17 |
| SGF |
2008 |
Labor-Saving SQL Constructs |
Brian P. Fairfield-Carter,
David W. Carr |
source |
10 |
| SGF |
2008 |
Practical Methods for Creating CDISC SDTM Data Sets from Existing Data |
Robert W. Graebner |
source |
11 |
| SGF |
2008 |
PROC TABULATE and the Neat Things You Can Do with It |
Wendi L. Wright |
source |
17 |
| SGF |
2008 |
SET, MERGE and beyond. |
Erik W. Tilanus |
source |
15 |
| SGF |
2008 |
Tabbing Through ODS II |
Haibin Shu |
source |
3 |
| SGF |
2008 |
The Art of Debugging |
H. Ian Whitlock |
source |
20 |
| SGF |
2008 |
The Essentials of SAS Dates and Times |
Derek P. Morgan |
source |
14 |
| SGF |
2008 |
The SAS INFILE and FILE Statements |
Steven J. First |
source |
28 |
| SGF |
2008 |
Tools of Miscalculation: Managing Missing Values with SAS |
William C. Murphy |
source |
4 |
| SGF |
2008 |
Try, Try Again: Replication-Based Variance Estimation Methods for Survey Data Analysis in SAS 9.2 |
Anthony B. An,
Pushpal K. Mukhopadhyay,
Randall D. Tobias,
Donna L. Watts |
source |
16 |
| SGF |
2008 |
Understanding Your Customer: Segmentation Techniques for Gaining Customer Insight and Predicting Risk in the Telecom Industry |
Glendon Cross,
Wayne Thompson |
source |
14 |
| NESUG |
2007 |
A Consolidated Macro for Iterative Hot Deck Imputation |
Bruce Ellis |
|
15 |
| NESUG |
2007 |
A Lazy Programmer Case Study: Dynamic Macro Code to Deal with Changing Number of Variables over Time |
Suzanne M. Dorinski |
|
14 |
| NESUG |
2007 |
Any Data Set to Excel, Dynamically |
Matthew Cohen |
|
14 |
| NESUG |
2007 |
Conditional Lags Don't Have to Be Treacherous |
Howard L. Schreier |
|
10 |
| NESUG |
2007 |
Conquering the Fear of Shift Tables |
Monal Kohli |
|
11 |
| NESUG |
2007 |
How To Produce Almost Perfect Excel Output |
Suzanne M. Dorinski |
|
15 |
| NESUG |
2007 |
Identify and Remove Any Variables, Character or Numeric, That Have Only Missing Values |
Wei Xu |
|
3 |
| NESUG |
2007 |
Implementing Multiple Imputation in an Automatic Variable Selection Scenario |
Krishna Mehta,
Mayank Rustagi,
Saurabh Kohli,
Siddharth Tiwari |
|
4 |
| NESUG |
2007 |
Importance of WARNING and NOTE Messages from SAS Log |
Jyotheeswara Yellanki |
|
5 |
| NESUG |
2007 |
Partially Transforming Hierarchical Data Sets for Sequential Processing Using Arrays |
Richard L. Downs Jr.,
Pura A. Perez |
|
9 |
| NESUG |
2007 |
PROC EXPAND to the Rescue! |
Premal Vora |
|
5 |
| NESUG |
2007 |
Summing with SAS |
Tatiana Homonoff |
|
12 |
| NESUG |
2007 |
Take Control of Your Output: Discover Dynamic Data Exchange |
Matthew Taylor |
|
7 |
| NESUG |
2007 |
The Most Important Efficiency Techniques |
Robert Virgile |
|
11 |
| NESUG |
2007 |
Undocumented and Hard-to-find PROC SQL Features |
Kirk Paul Lafler |
|
11 |
| NESUG |
2007 |
Working with Missing Values |
Edward Heaton,
Michelle Zhuang |
|
3 |
| SESUG |
2007 |
Application of an Exact Covariate Matching Macro in Outcome Analysis |
Guiping Yang |
source |
19 |
| SESUG |
2007 |
Generating Random Numbers from a Discrete User Defined Distribution |
Amy Boulris |
source |
6 |
| SESUG |
2007 |
SAS Formats, More Than Just Another Pretty Face |
Ying Liu |
source |
15 |
| SESUG |
2007 |
Set, Match, Merge ... Don't You Love SAS |
Peter W. Eberhardt,
Ying Liu |
source |
19 |
| SESUG |
2007 |
The DOW-Loop Unrolled |
Paul M. Dorfman,
Lessia S. Shajenko |
source |
18 |
| SESUG |
2007 |
Using INFILE and INPUT Statements to Introduce External Data into the SAS System |
Andrew T. Kuligowski |
source |
18 |
| MWSUG |
2007 |
Advanced PROC REPORT: Doing More in the Compute Block |
Arthur L. Carpenter |
|
23 |
| MWSUG |
2007 |
Identifying and Overcoming Common Data Mining Mistakes |
Doug Wielenga |
|
20 |
| MWSUG |
2007 |
Using Data Mining Techniques to Predict Student Development and Retention |
Morgan C. Wang |
|
59 |
| WUSS |
2007 |
A Datum to Remember: Using the RETAIN Statement |
Jeanina M. Worden,
Chunmei Yang |
source |
10 |
| WUSS |
2007 |
A Simple Way to Create a Running Total |
David Fickbohm |
source |
7 |
| WUSS |
2007 |
Advanced PROC REPORT: Doing More in the Compute Block |
Arthur L. Carpenter |
source |
23 |
| WUSS |
2007 |
BFF: Binary Flags Forever (Or, Boole's Paradise) |
David J. Pasta,
Eric P. Elkin |
source |
8 |
| WUSS |
2007 |
Data Mining Using SAS Enterprise Miner |
Randall Matignon |
source |
10 |
| WUSS |
2007 |
Hypothesis Testing: An SQL Analogy |
Leroy Bracken,
Paul D. Sherman |
source |
5 |
| WUSS |
2007 |
MISSING VALUES: Everything You Ever Wanted to Know |
Malachy J. Foley |
source |
9 |
| WUSS |
2007 |
SAS and Plug-Ins, Extensibility, Scalability and Execution Tracing in the SAS Environment; A SAS Developer's Approach in Applying Web Development Concepts in SAS |
Joe Perry |
source |
11 |
| WUSS |
2007 |
Tap Into the Power of Formats |
Nikki M. Carroll |
source |
12 |
| WUSS |
2007 |
The TRANSPOSE Procedure or How to Turn your Data Around |
David Fickbohm |
source |
10 |
| WUSS |
2007 |
Time Series Cross Sectional Analysis of Total Nitrogen Concentration in Truckee River, Nevada Using SAS |
Anpalaki J. Ragavan |
source |
11 |
| PHUSE EU |
2007 |
The Good, the Bad and the Ugly of programming standard |
Rebecca Tootell,
Natalie Collier |
source |
1 |
| SCSUG |
2007 |
A Hands-On Introduction to Using PROC TABULATE |
Thomas J. Winn Jr. |
|
20 |
| SCSUG |
2007 |
Explaining Unexpected Log Messages and Output Results from DATA Step Code |
Debbie Buck,
Larry Stewart |
|
13 |
| SCSUG |
2007 |
SAS Formats - Simply Powerful |
Howard Hagemann |
|
12 |
| SCSUG |
2007 |
The Impact of Data Visualization in a Study of Chronic Disease |
Gabe Cano,
Brad Smith,
Paul Cuddihy |
|
17 |
| SCSUG |
2007 |
Undocumented and Hard-to-find Features in PROC SQL |
Kirk Paul Lafler |
|
10 |
| SCSUG |
2007 |
Using INFILE and INPUT Statements to Introduce External Data into the SAS System |
Andrew T. Kuligowski |
|
18 |
| PNWSUG |
2007 |
Missing Data? A Look at Two Imputation Methods |
Anita Rocha |
|
8 |
| PNWSUG |
2007 |
Undocumented and Hard-to-find PROC SQL Features |
Kirk Paul Lafler |
|
10 |
| PNWSUG |
2007 |
Using INFILE and INPUT Statements to Introduce External Data into the SAS System |
Andrew T. Kuligowski |
|
18 |
| PNWSUG |
2007 |
Utilizing PHREG for Cox proportional hazards regression modeling with time-varying covariates |
Marla J. Husnik,
Eugene Huang |
|
14 |
| PharmaSUG |
2007 |
A Review of Propensity Score Application in Healthcare Outcome and Epidemiology |
Guiping Yang |
|
24 |
| PharmaSUG |
2007 |
Advanced PROC REPORT: Doing More in the Compute Block |
Arthur L. Carpenter |
|
21 |
| PharmaSUG |
2007 |
Basic Defensive Programming Techniques |
Baoxian Lan,
Daniel Tsui |
|
11 |
| PharmaSUG |
2007 |
From CRF Data to DEFINE.XML: Going "End to End" with Metadata |
Frank C. DiIorio,
Jeffrey M. Abolafia |
|
20 |
| PharmaSUG |
2007 |
Growing Growth Curves Using PROC MIXED and PROC NLMIXED |
Jeffrey M. Gossett,
Pippa Simpson,
Patrick Casey,
Leanne Whiteside-Mansell,
Robert Bradley,
Chan-Hee Jo |
|
10 |
| PharmaSUG |
2007 |
Hypothesis Testing: An SQL Analogy |
Leroy Bracken,
Boulder Creek |
|
5 |
| PharmaSUG |
2007 |
SAS Macro: Symbols of Frustration? %Let us help! A Guide to Debugging Macros |
Arthur L. Carpenter |
|
20 |
| PharmaSUG |
2007 |
SAS Markov Chain Monte Carlo (MCMC) Simulation in Practice |
Shi-Tao Yeh,
Scott D Patterson |
|
20 |
| PharmaSUG |
2007 |
Strategies and Practical Considerations for Creating CDISC SDTM Domain Data Sets from Existing CDM Data Sets |
Robert W. Graebner |
|
12 |
| PharmaSUG |
2007 |
Undocumented and Hard-to-find PROC SQL Features |
Kirk Paul Lafler |
|
10 |
| SGF |
2007 |
Advanced PROC REPORT: Doing More in the Compute Block |
Arthur L. Carpenter |
source |
22 |
| SGF |
2007 |
Data Matching |
David H. Dickinson,
Wendy B. Johnson |
source |
18 |
| SGF |
2007 |
Data Mining to Investigate the Prescribing of Medications Longitudinally |
Mussie A. Tesfamicael |
source |
17 |
| SGF |
2007 |
How Suite It Is - Taking Full Advantage of SAS Seamless Integration with Microsoft's Office Suite |
Michael L. Hennessey,
John C. Compson |
source |
12 |
| SGF |
2007 |
How to Think Through the SAS DATA Step |
H. Ian Whitlock |
source |
19 |
| SGF |
2007 |
Identifying and Overcoming Common Data Mining Mistakes |
Doug Wielenga |
source |
20 |
| SGF |
2007 |
Macro Bugs: How to Create, Avoid, and Destroy Them |
H. Ian Whitlock |
source |
20 |
| SGF |
2007 |
Reading Excel Workbooks |
Edward Heaton |
source |
13 |
| SGF |
2007 |
SAS and Sudoku |
Richard A. DeVenezia,
John R. Gerlach,
Larry Hoyle,
Talbot M. Katz,
Richard D. Langston |
source |
37 |
| SGF |
2007 |
SAS Macros for Automated Model Selection When the Response Variable Is Nominal-Needs and Solutions Involving PROC CATMOD |
Bonnie E. Williams,
Todd Kegan |
source |
12 |
| SGF |
2007 |
The Electronic Project: Effectively Using Metadata throughout the Project Life Cycle |
Jeffrey M. DiIorio,
Frank Abolafia |
source |
18 |
| SGF |
2007 |
The Most Important Efficiency Techniques |
Robert Virgile |
source |
11 |
| SGF |
2007 |
Using PROC FORMAT for Data Validation and Clean-Up |
Christianna S. Williams |
source |
14 |
| SGF |
2007 |
Using the DATASETS Procedure - Part II |
David Fickbohm |
source |
10 |
| SGF |
2007 |
Web-based Data Quality Assurance Reports at the National Alzheimers Coordinating Center (NACC) |
Woodrow Beekly,
Duane Lee,
William W. Koepsell,
Thomas Kukull,
Walter A. Deitrich |
source |
6 |
| SGF |
2007 |
Win the Pennant! Use PROC FORMAT |
Marge Scerbo |
source |
13 |
| PNWSUG |
2006 |
Five Easy (To Use) Macros |
Ted Clay |
|
32 |
| PNWSUG |
2006 |
Missing Data? A Look at Two Imputation Methods |
Anita Rocha |
|
2 |
| PNWSUG |
2006 |
The Power of PROC FORMAT |
Jonas V. Bilenas |
|
9 |
| MWSUG |
2006 |
Avoiding Blind Dates with SAS |
Derek P. Morgan |
|
14 |
| SCSUG |
2006 |
An Introduction to SQL in SAS |
Peter J. Lund |
|
22 |
| SCSUG |
2006 |
MISSING VALUES: Everything You Ever Wanted to Know |
Malachy J. Foley |
|
9 |
| PHUSE EU |
2006 |
Analysis of incomplete two-period crossover trials with SAS PROC MIXED |
Jochen Müller-Cohrs |
source |
8 |
| PHUSE EU |
2006 |
Defensive Programming Techniques |
Jennie McGuirk,
John Woods |
source |
5 |
| PHUSE EU |
2006 |
Partial Dates; decisions and implications of handling partially missing dates |
Rachel Bowman |
source |
6 |
| PHUSE EU |
2006 |
The Power of RETAIN Statement in Clinical Trial Programming by Examples |
Baoxian Lan |
source |
5 |
| SESUG |
2006 |
%IFN - A Macro Function |
H. Ian Whitlock |
source |
6 |
| SESUG |
2006 |
A Relational Understanding of SDTM Tables |
John R. Gerlach |
source |
10 |
| SESUG |
2006 |
An Annotated Guide: Using Proc Tabulate And Proc Summary to Validate SAS Code |
Russell M. Lavery |
source |
13 |
| SESUG |
2006 |
Data Step Interfaces with the Macro Facility |
Toby Dunn |
source |
17 |
| SESUG |
2006 |
ET06 DATALINES, Sequential Files, CSV, HTML and More - Using INFILE and INPUT Statements to Introduce External Data into the SAS System |
Andrew T. Kuligowski |
source |
18 |
| SESUG |
2006 |
How to Think Through the SAS DATA Step |
H. Ian Whitlock |
source |
20 |
| SESUG |
2006 |
One Method for Improving Quality of Data in Longitudinal Study |
Milorad Stojanovic,
Ruth Heuer |
source |
6 |
| SESUG |
2006 |
Sudoku Puzzles - Using a Cube to Solve the Square |
John R. Gerlach |
source |
10 |
| SESUG |
2006 |
Top-Down Programming with SAS Macros |
Edward Heaton |
source |
18 |
| SESUG |
2006 |
Using Advanced Features of DDE to Customize Formatted Tables in Excel |
Dennis J. Beal,
Oak Ridge |
source |
16 |
| WUSS |
2006 |
Errors, Warnings, and Notes (Oh My) A Practical Guide to Debugging SAS Programs |
Lora D. Delwiche,
Susan J. Slaughter |
source |
15 |
| WUSS |
2006 |
Examples of Data Manipulation in SAS: Filling in "Known" Missing Data |
Chong Ho Yu |
source |
5 |
| WUSS |
2006 |
Missing Fun with Functions |
Kathryn A. Valdes |
source |
5 |
| WUSS |
2006 |
PROC SQL: A Powerful Tool to Improve Your Data Quality |
Keh-Dong Shiang |
source |
10 |
| WUSS |
2006 |
Steps to Success with PROC MEANS |
Andrew H. Karp |
source |
12 |
| WUSS |
2006 |
The Logic and Logistics of Logistic Regression |
Lawrence Rasouliyan,
Dave P. Miller |
source |
14 |
| WUSS |
2006 |
What's the DIF? |
Nikki M. Carroll |
source |
4 |
| NESUG |
2006 |
%IFN - A Macro Function |
Chang Y. Chung,
H. Ian Whitlock |
|
6 |
| NESUG |
2006 |
%MISSING: A Macro to Report Missing Value Percentages for a Multi-Year Multi-File Information System |
Rushi Patel |
|
6 |
| NESUG |
2006 |
How To Produce Almost Perfect RTF Output |
Suzanne M. Dorinski |
|
20 |
| NESUG |
2006 |
How to Reduce the Disk Space Required by a SAS Dataset |
Selvaratnam Sridharma |
|
16 |
| NESUG |
2006 |
Introduction to PROC TABULATE |
Wendi L. Wright |
|
23 |
| NESUG |
2006 |
LAG - the Very Powerful and Easily Misused SAS Function |
Yunchao (Susan) Tian |
|
5 |
| NESUG |
2006 |
SAS Interview or "How to Get a Second Date" |
Gary M. McQuown |
|
7 |
| NESUG |
2006 |
The Power of PROC FORMAT |
Jonas V. Bilenas |
|
9 |
| NESUG |
2006 |
Those Sneaky SAS Functions: Beware of Unexpected Handling of Missing Data and Variable Lists |
Christianna S. Williams |
|
4 |
| PharmaSUG |
2006 |
Murphy was an optimist: a realistic approach to clinical trial data |
Pamela L. Reading |
|
4 |
| PharmaSUG |
2006 |
PROC FORMAT is Our Friend |
Erin M. Christen |
|
20 |
| PharmaSUG |
2006 |
Stellar Graphics Using SAS (but not necessarily SAS/GRAPH) |
P. Chris Holland,
Yan Zhu |
|
12 |
| PharmaSUG |
2006 |
The Utter Simplicity of the TABULATE Procedure |
Daniel E. Bruns |
|
17 |
| SUGI |
2006 |
%DataAutoSum: A SAS Macro for Automatically Producing a Customized Clinical Data Summary Report |
Deqing Pei,
Cheng Cheng |
source |
8 |
| SUGI |
2006 |
%IFN - A Macro Function |
Chang Y. Chung,
H. Ian Whitlock |
source |
6 |
| SUGI |
2006 |
DATALINES, Sequential Files, CSV, HTML, and More - Using INFILE and INPUT Statements to Introduce External Data into the SAS System |
Andrew T. Kuligowski |
source |
18 |
| SUGI |
2006 |
Exploratory or Confirmatory Factor Analysis? |
Diana D. Suhr |
source |
17 |
| SUGI |
2006 |
FREQ n' MEANS |
Marge Scerbo,
Michael S. Lajiness |
source |
19 |
| SUGI |
2006 |
Graphing Laboratory Data and an Introduction to the Custom Macro %GRAPHME |
Adrienne Groulx |
source |
7 |
| SUGI |
2006 |
How to Think Through the SAS DATA Step |
H. Ian Whitlock |
source |
20 |
| SUGI |
2006 |
Investigating Open Source Project Success - A Data Mining Approach to Model Formulation, Testing and Validation |
Uzma Raja,
Marietta J. Tretter |
source |
7 |
| SUGI |
2006 |
MISSING! - Understanding and Making the Most of Missing Data |
Suzanne M. Humphreys |
source |
9 |
| SUGI |
2006 |
Modeling Water Quality Trend in Long Term Time Series |
Anpalaki J. Ragavan,
George C. J. Fernandez |
source |
10 |
| SUGI |
2006 |
PROC SQL: A Powerful Tool to Improve Your Data Quality |
Keh-Dong Shiang |
source |
10 |
| SUGI |
2006 |
So, Your Data are in Excel! |
Edward Heaton |
source |
13 |
| SUGI |
2006 |
SQL Set Operators: So Handy Venn You Need Them |
Howard L. Schreier |
source |
18 |
| SUGI |
2006 |
The Enigma of Survey Data Analysis: Comparison of SAS Survey Procedures and SUDAAN Procedures |
Katherine L. Baisden,
Paul Hu |
source |
11 |
| SESUG |
2005 |
%CHECKDATA: An Enhanced Data Diagnostic Macro |
John Stanmeyer |
source |
8 |
| SESUG |
2005 |
_05 Using MS-ACCESS Metadata to Drive Automated SAS Data |
Gary N. Weeks |
source |
3 |
| SESUG |
2005 |
A Tour of the SAS Reporting Toolbox |
Frank C. DiIorio |
source |
20 |
| SESUG |
2005 |
Five Ways to Create Macro Variables: A Short Introduction to the Macro Language |
Arthur L. Carpenter |
source |
12 |
| SESUG |
2005 |
Fun With Functions |
Marje Fecht |
source |
5 |
| SESUG |
2005 |
How SAS Thinks |
Neil H. Howard |
source |
19 |
| SESUG |
2005 |
Macro Bugs - How to Create, Avoid, and Destroy Them |
H. Ian Whitlock |
source |
20 |
| SESUG |
2005 |
Manipulating Data: Elements of the DATA Step Language |
Paul M. Dorfman |
source |
19 |
| SESUG |
2005 |
MISSING VALUES: Everything You Ever Wanted to Know |
Malachy J. Foley |
source |
8 |
| SESUG |
2005 |
So, Your Data are in Excel! |
Edward Heaton |
source |
13 |
| SESUG |
2005 |
The Functionality of Arithmetic SAS Functions for the CRO User |
Philip J. d'Almada,
Karen Graham |
source |
5 |
| SESUG |
2005 |
The Missing Link: Data Analysis with Missing Information |
Venita DePuy |
source |
9 |
| SESUG |
2005 |
The Power of PROC FORMAT |
Jonas V. Bilenas |
source |
8 |
| PHUSE EU |
2005 |
Empty lines in SAS tables (list output and ODS RTF) produced by Proc Tabulate |
Twan van Berkel |
source |
15 |
| MWSUG |
2005 |
Mining Transactional And Time Series Data |
Michael J. Leonard |
|
26 |
| WUSS |
2005 |
A Comparison of Missing Data Handling Methods |
Catherine Truxillo |
source |
20 |
| WUSS |
2005 |
A Meeting of the Minds |
Kathryn A. Valdes |
source |
7 |
| WUSS |
2005 |
Can Matrix Capture Missing Data Values? |
Keh-Dong Shiang |
source |
9 |
| WUSS |
2005 |
Effects of PROC EXPAND Data Interpolation on Time Series Modeling when the Data are Volatile or Complex |
Keiko I. Powers |
source |
7 |
| WUSS |
2005 |
Get It Together: Combining Data with SAS MERGE, UPDATE, and SET |
Mel Widawski |
source |
12 |
| WUSS |
2005 |
Habits that Help: Developing Good Programming Style |
Casey Cantrell |
source |
13 |
| WUSS |
2005 |
MISSING VALUES: Everything You Ever Wanted to Know |
Malachy J. Foley |
source |
8 |
| WUSS |
2005 |
SAS Essentials |
Neil H. Howard |
source |
19 |
| WUSS |
2005 |
Using Proc Mixed to Model Daytime Log-Cortisol Slopes |
Eric Neri,
Helena Chmura Kraemer,
Arthur M. Noda,
Yung Wang |
source |
4 |
| NESUG |
2005 |
"LAG With a WHERE" and Other DATA Step Stories |
Neil H. Howard |
|
14 |
| NESUG |
2005 |
A SAS Programmer's Guide to Project - and Program-Level Quality Control |
Paul Gorrell |
|
15 |
| NESUG |
2005 |
A Time Machine: Using SAS Macros |
Adel Fahmy |
|
13 |
| NESUG |
2005 |
Array Programming Basics |
John J. Cohen |
|
11 |
| NESUG |
2005 |
Beyond FORMAT Basics |
Michael S. Zdeb |
|
20 |
| NESUG |
2005 |
Freq n' Means |
Marge Scerbo,
Michael S. Lajiness |
|
19 |
| NESUG |
2005 |
Fun With Functions |
Marje Fecht |
|
5 |
| NESUG |
2005 |
Handling Different Rules for the Imputation of Missing Data Using an LOCF Approach |
Mark Keese |
|
5 |
| NESUG |
2005 |
How SAS Thinks |
Neil H. Howard |
|
19 |
| NESUG |
2005 |
In Search of the LOST CARD |
Andrew T. Kuligowski |
|
9 |
| NESUG |
2005 |
Loop-Do-Loop Around Arrays |
Wendi L. Wright |
|
16 |
| NESUG |
2005 |
Personalizing Mailings and Driving Sales Using SAS/GRAPH |
James P. Shields |
|
10 |
| NESUG |
2005 |
The INPUT Statement: Where It's @ |
Ronald P. Cody |
|
13 |
| NESUG |
2005 |
The Power of PROC FORMAT Updated for SAS9. |
Jonas V. Bilenas |
|
8 |
| NESUG |
2005 |
The Survey Data Book an Easy Read via Proc Tabulate and the ODS RTF Destination |
Electra Small |
|
11 |
| PharmaSUG |
2005 |
Don't Stay up Late Completing your Late-Phase Analysis! |
Kathleen Kushner |
|
8 |
| PharmaSUG |
2005 |
How SAS Thinks |
Neil H. Howard |
|
20 |
| PharmaSUG |
2005 |
Processing Name/Value Input in SAS Web Applications |
Don Boudreaux |
|
5 |
| PharmaSUG |
2005 |
Quality Testing for Programs Used in Regulatory Submission |
Xiaohui Wang,
Elaine Czarnecki |
|
4 |
| PharmaSUG |
2005 |
Retaining, Lagging, Leading, and Interleaving Data |
Toby Dunn,
Chang Y. Chung |
|
8 |
| PharmaSUG |
2005 |
Risk-Based Approach to SAS Program Validation |
Keith C. Benze |
|
7 |
| PharmaSUG |
2005 |
Rules for Tools - The SAS Utility Primer |
Frank C. DiIorio |
|
19 |
| PharmaSUG |
2005 |
Selected SAS Macro and SQL Techniques For Efficient Analysis Programming |
Brian P. Fairfield-Carter |
|
12 |
| PharmaSUG |
2005 |
Speed up your SAS Programs |
Karthikeyan Chidambaram |
|
5 |
| SUGI |
2005 |
A Macro to Calculate Kappa Statistics for Categorizations by Multiple Raters |
Bin Chen,
Lynn Seel,
Dennis Zaebst |
source |
6 |
| SUGI |
2005 |
An Introduction to SQL in SAS |
Pete Lund - SC |
source |
22 |
| SUGI |
2005 |
Automating Predictive Analysis to Predict Medicare Fraud |
Christine L. Warner |
source |
9 |
| SUGI |
2005 |
Building a Data-Driven Computer Assisted Interview Using SAS/AF |
Derek P. Morgan |
source |
19 |
| SUGI |
2005 |
Data Profiling: Designing the Blueprint for Improved Data Quality |
Brett Dorr,
Pat M. Herbert |
source |
10 |
| SUGI |
2005 |
Explaining Unexpected Log Messages and Output Results from DATA Step Code |
Debbie Buck - SC,
Larry Stewart |
source |
13 |
| SUGI |
2005 |
Imputation of Categorical Variables with PROC MI |
Paul D. Allison |
source |
14 |
| SUGI |
2005 |
In Search of the LOST CARD |
Andrew T. Kuligowski |
source |
9 |
| SUGI |
2005 |
IRT-FIT: SAS Macros for Fitting Item Response Theory (IRT) Models |
Robert Terry,
Sung-Hyuck Lee |
source |
9 |
| SUGI |
2005 |
Let Your Data Power Your DATA Step: Making Effective Use of the SET, MERGE, UPDATE, and MODIFY Statements |
Howard L. Schreier |
source |
19 |
| SUGI |
2005 |
Litigation and SAS: Some Dos and Don'ts (or, You Call that Evidence?) |
Nicholson E. Warman |
source |
10 |
| SUGI |
2005 |
Macro Bugs - How to Create, Avoid, and Destroy Them |
H. Ian Whitlock |
source |
20 |
| SUGI |
2005 |
Maximum Likelihood Parameter Estimation with Incomplete Data |
Catherine Truxillo |
source |
19 |
| SUGI |
2005 |
Mining Transactional And Time Series Data |
Michael J. Leonard,
Brenda Wolfe |
source |
26 |
| SUGI |
2005 |
More Than Batch: A Production SAS Framework |
Denis L. Cogswell |
source |
10 |
| SUGI |
2005 |
PROC FREQ and PROC MEANS: To Stat or Not to Stat |
Marge Scerbo,
Michael S. Lajiness |
source |
17 |
| SUGI |
2005 |
Rounding after Multiple Imputation with Non-Binary Categorical Covariates |
Christopher F. Ake |
source |
11 |
| SUGI |
2005 |
Rule Your Data with The Link King (a SAS/AF application for record linkage and unduplication) |
Kevin M. Campbell |
source |
9 |
| SUGI |
2005 |
Rules for Tools - The SAS Utility Primer |
Frank C. DiIorio |
source |
19 |
| SUGI |
2005 |
SAS with the Windows API |
David H. Johnson |
source |
20 |
| SUGI |
2005 |
Tagset Spelunking and Cartography: Debugging and Exploring Tagsets with Battery-Powered Headlamps |
Eric A. Gebhart |
source |
15 |
| SUGI |
2005 |
The Utter Simplicity and Power of the TABULATE Procedure |
Daniel E. Bruns |
source |
14 |
| SUGI |
2005 |
Using XML Mapper and XMLMAP to Read Data Documented by Data Documentation Initiative (DDI) Files |
Larry Hoyle |
source |
10 |
| NESUG |
2004 |
A SAS Macro for Naive Bayes Classification |
Vadim Pliner |
|
6 |
| NESUG |
2004 |
Analyzing Complex Survey Data: Some SAS-SUDAAN Comparisons |
Xiuhua Chen,
Paul Gorrell |
|
15 |
| NESUG |
2004 |
Array Tutorial (2) $ Beginning Intermediate; |
Marge Scerbo |
|
11 |
| NESUG |
2004 |
Clinical Trial Data Validation: Using PROC SQL Effectively |
Balakrishna Dandamudi |
|
20 |
| NESUG |
2004 |
How SAS Thinks or Why the DATA Step Does What It Does |
Neil H. Howard |
|
19 |
| NESUG |
2004 |
How Smooth Is (PROC) SYLK' |
Howard L. Schreier |
|
23 |
| NESUG |
2004 |
SAS MACROS: Offer the Best Dating Service |
Adel Fahmy |
|
9 |
| NESUG |
2004 |
Take an In-depth Look at %EVAL Function |
Beilei Xu,
Lei Zhang |
|
6 |
| NESUG |
2004 |
The INPUT Statement: Where It's @ |
Ronald P. Cody |
|
13 |
| SCSUG |
2004 |
A Complete Guide to HTML Form Output for SAS/IntrNet Developers |
Don Boudreaux |
|
6 |
| SCSUG |
2004 |
Retaining, Lagging, Leading, and Interleaving Data |
Toby Dunn |
|
14 |
| SCSUG |
2004 |
SAS/ACCESS to External Databases: Wisdom for the Warehouse User |
Judy Loren |
|
10 |
| SCSUG |
2004 |
Teacher Evaluations - A tutorial in some advanced features of SAS |
Glenn Millard |
|
19 |
| SCSUG |
2004 |
Undocumented and Hard-to-find SQL Features |
Kirk Paul Lafler |
|
10 |
| SESUG |
2004 |
A Simulation Study to Evaluate ANOVA and GEE for Comparing Correlated Proportions with Missing Values |
Mark S. Litaker |
source |
6 |
| SESUG |
2004 |
Here's the Data, Here's the Report I Want - How Do I Get There? |
Deborah B. Buck |
source |
8 |
| SESUG |
2004 |
How to Incorporate Old SAS Data into a New DATA Step, or "What is S-M-U?" |
Andrew T. Kuligowski |
source |
13 |
| SESUG |
2004 |
Merging: Avoid the Cones |
Jessica Bonow,
Little Rock |
source |
5 |
| SESUG |
2004 |
Rules for Tools - The SAS Utility Primer |
Frank C. DiIorio |
source |
18 |
| SESUG |
2004 |
Stata for the Struggling SAS Mind |
Dan Blanchette |
source |
11 |
| SESUG |
2004 |
Using Dynamic Data Exchange to Customize Formatted Reports in Microsoft Excel |
Dennis J. Beal |
source |
19 |
| SESUG |
2004 |
Using SAS to Facilitate Data Comparisons |
M. Rita Thissen,
Elizabeth Heath |
source |
12 |
| SESUG |
2004 |
Why the DATA Step Does What It Does |
Neil H. Howard |
source |
19 |
| WUSS |
2004 |
Five Ways to Create Macro Variables: A Short Introduction to the Macro Language |
Arthur L. Carpenter |
source |
12 |
| WUSS |
2004 |
How to Add to Existing Data or, Why Did You Do it That Way? |
Kathryn A. Valdes |
source |
14 |
| WUSS |
2004 |
It's a P-value, All Right - but is it the Right One? |
Linda Collins |
source |
6 |
| WUSS |
2004 |
Macros To Report Missing Data: An HTML Data Collection Guide |
Patrick Thornton |
source |
19 |
| WUSS |
2004 |
Next Generation Data Warehousing with SAS 9 |
Gary Mehler |
source |
10 |
| WUSS |
2004 |
Speed up your SAS Programs |
Karthikeyan Chidambaram |
source |
5 |
| WUSS |
2004 |
Taming Your Character Data with Regular Expressions in SAS - Part II |
Mary McCracken,
James J. Van Campen |
source |
5 |
| WUSS |
2004 |
The Continuing Quandary of Survey Data Part II:Comparison of SAS Procedures and SUDAAN Procedures |
Katherine L. Baisden |
source |
20 |
| WUSS |
2004 |
Time Series Magic: Using PROC EXPAND with Time Series Data |
Andrew H. Karp |
source |
8 |
| MWSUG |
2004 |
Using SAS Special Missing Values to Enhance Display and Analysis of Survey Research Data |
Francine Cannarozzi |
|
5 |
| PNWSUG |
2004 |
Complex Sampling Meets the Blazing Turkey of Glory |
David L. Cassell |
|
8 |
| PNWSUG |
2004 |
Dynamically Change Your Perspective with the Link King (and a Little SAS/AF) |
Kevin M. Campbell |
|
9 |
| PharmaSUG |
2004 |
"LAG With a WHERE" and Other DATA Step Stories |
Neil H. Howard |
|
14 |
| PharmaSUG |
2004 |
A Technique to Create a Numeric Variable from a Character Variable |
Hany Aboutaleb |
|
6 |
| PharmaSUG |
2004 |
Five Ways to Create Macro Variables: A Short Introduction to the Macro Language |
Arthur L. Carpenter |
|
12 |
| PharmaSUG |
2004 |
How SAS Thinks or Why the DATA Step Does What It Does |
Neil H. Howard |
|
17 |
| PharmaSUG |
2004 |
Incremental Methods of Imputation for Longitudinal Data with Informative Missingness |
Naum M. Khutoryansky |
|
4 |
| PharmaSUG |
2004 |
Undocumented and Hard-to-find SQL Features |
Kirk Paul Lafler |
|
9 |
| PharmaSUG |
2004 |
Why SAS is the Best Place to Put Your Clinical Data |
Steven A. Wilson |
|
12 |
| SUGI |
2004 |
A Lightweight HTML Codebook Generator for Clinical Trial Data |
Lei Zhang |
source |
11 |
| SUGI |
2004 |
An Introduction to SAS Function-ality |
Deb Cassidy |
source |
13 |
| SUGI |
2004 |
Array Tutorial (2) $ Beginning Intermediate; |
Marge Scerbo |
source |
12 |
| SUGI |
2004 |
Ask This Old Newsgroup: Using SAS-L Effectively |
Howard L. Schreier |
source |
11 |
| SUGI |
2004 |
Debugging 101 |
Peter Knapp |
source |
15 |
| SUGI |
2004 |
How SAS Thinks or Why the DATA Step Does What It Does |
Neil H. Howard |
source |
19 |
| SUGI |
2004 |
How to Incorporate Old SAS Data into a New DATA Step, or "What is S-M-U?" |
Andrew T. Kuligowski |
source |
14 |
| SUGI |
2004 |
Mrs. Clean Tackles Dirty Data |
Janet E. Stuelpner |
source |
5 |
| SUGI |
2004 |
Next Generation Data Warehousing with SAS 9 |
Gary Mehler |
source |
11 |
| SUGI |
2004 |
PROC FREQ - What's It Really Good For? |
Theresa Gordon,
Monique Eleby |
source |
4 |
| SUGI |
2004 |
SAS Macro: Symbols of Frustration? %Let us help! A Guide to Debugging Macros |
Kevin Payton Delaney,
Arthur L. Carpenter |
source |
21 |
| SUGI |
2004 |
Some Statistical Strategies for Analyzing Confirmatory Studies Involving One or More Occurrences of Primary Events |
Gary G. Koch,
Maura E. Stokes |
source |
21 |
| SUGI |
2004 |
Some Uses (and Handy Abuses) of PROC TRANSPOSE |
Ralph W. Leighton |
source |
17 |
| SUGI |
2004 |
Steps to Success with PROC MEANS |
Andrew H. Karp |
source |
13 |
| SUGI |
2004 |
The INPUT Statement: Where It's @ |
Ronald P. Cody |
source |
14 |
| SUGI |
2004 |
The Utter "Simplicity?" of the TABULATE Procedure - The Final Chapter? |
Daniel E. Bruns |
source |
14 |
| SUGI |
2004 |
Using SAS Procedures to Make Sense of a Complex Food Store Survey |
Jeffrey M. Gossett,
Pippa M. Simpson,
Renée A. Hall,
Carol Connell,
Kathy Yadrick,
Margaret L. Bogle |
source |
8 |
| SUGI |
2004 |
Why SAS is the Best Place to Put Your Clinical Data |
Steven A. Wilson |
source |
13 |
| WUSS |
2003 |
Answering Your Questions with Statistics |
Diana D. Suhr |
source |
6 |
| WUSS |
2003 |
Errors, Warnings, and Notes (Oh My) A Practical Guide to Debugging SAS Programs |
Lora D. Delwiche,
Susan J. Slaughter |
source |
10 |
| WUSS |
2003 |
Extending the Use of PROC PHREG in Survival Analysis |
Christopher F. Ake,
Arthur L. Carpenter |
source |
6 |
| WUSS |
2003 |
Macros To Report Missing Data: An HTML Data Collection Guide |
Patrick Thornton |
source |
6 |
| WUSS |
2003 |
Methods of Producing Datasets with Unique Record per Id Number |
Alexander Khartchenko,
Irina V. Tolstykh,
Natalia V. Udaltsova |
source |
4 |
| WUSS |
2003 |
SAS/AF, Metadata and the National Cancer Institute's Diet History Questionnaire: How to Build a Computer Assisted Interview |
Derek P. Morgan |
source
zip |
6 |
| WUSS |
2003 |
Taming Your Metadata Part I: Getting data dictionary and codebook from your SAS databases |
Peter Godard |
source |
6 |
| WUSS |
2003 |
The Quandary of Survey Data: Comparison of SAS Procedures and SUDAAN Procedures |
Katherine L. Baisden |
source |
6 |
| WUSS |
2003 |
Using PROC EXPAND with Time Series Data |
Andrew H. Karp |
source |
7 |
| WUSS |
2003 |
Why SAS is the Best Place to Put Your Clinical Data |
Steven A. Wilson |
source |
10 |
| SCSUG |
2003 |
FUZZY MERGES: Examples and Techniques |
Malachy J. Foley |
|
10 |
| SCSUG |
2003 |
JavaTM Syntax for SAS Programmers |
Don Boudreaux |
|
6 |
| SCSUG |
2003 |
Macros - Basic |
Destiny Corporation (a company) |
|
14 |
| SCSUG |
2003 |
SQL - Intermediate |
Destiny Corporation (a company) |
|
19 |
| SESUG |
2003 |
Developing a Marketing Geographic Segmentation System Using SAS Software |
Kellie M. Poulin,
Allison N. Freeman |
source |
10 |
| SESUG |
2003 |
Excel Exposed: Using Dynamic Data Exchange to Extract Metadata from MS Excel Workbooks |
Koen Vyverman |
source |
10 |
| SESUG |
2003 |
Macros - Basic |
Destiny Corporation (a company) |
|
14 |
| SESUG |
2003 |
Missing Data Values: Analyzing their Effects on Rainfall Forecasts Using PROC EXPAND and the SAS Time-Series Forecasting System |
Richard A. March |
source |
4 |
| SESUG |
2003 |
Sublime Secrets of the SAS SQLheads |
Sigurd W. Hermansen |
source |
6 |
| SESUG |
2003 |
Too Many Choices! The IMPORTANCE of Rank-Ordering Independent Variables Prior to Exploratory Data Analysis |
Gregg Weldon |
source |
5 |
| SESUG |
2003 |
What is Missing in SAS? |
Imelda C. Go |
source |
5 |
| MWSUG |
2003 |
Beginning Macros |
Destiny Corporation (a company) |
|
14 |
| MWSUG |
2003 |
Missing observations: Identification and resolutions using the SAS System |
Doug Moore,
Tahereh Nourbakhsh |
|
14 |
| MWSUG |
2003 |
Using SAS Special Missing Values to Enhance Display and Analysis of Survey Research Data |
Francine Cannarozzi |
|
3 |
| NESUG |
2003 |
A SAS Program for Estimating Incidence Density for Cohort Studies with Multiple Follow-ups |
Jinbing Zhang,
Xiaofang Yu |
|
5 |
| NESUG |
2003 |
Debugging 101 |
Peter Knapp |
|
14 |
| NESUG |
2003 |
Developing Custom Analytic Tasks for Enterprise Guide |
Joe Carter,
Stephen McDaniel,
Mike Porter |
|
6 |
| NESUG |
2003 |
PROC MI as the Basis for a Macro for the Study of Patterns of Missing Data |
Carl E. Pierchala |
|
8 |
| NESUG |
2003 |
Quality Control With SAS Numeric Data |
Paul Gorrell |
|
11 |
| NESUG |
2003 |
The Dark Side of the Transparent Moon -- Tips, Tricks and Traps of Handling ORACLE Data Using SAS |
Xinyu Ji |
|
11 |
| NESUG |
2003 |
Tips for Manipulating Data |
Marge Scerbo |
|
10 |
| NESUG |
2003 |
Using ODS to Create Web-Based Neighborhood Profiles for the District of Columbia |
Peter Tatian |
|
7 |
| SEUGI |
2003 |
Data Mining for Predicting Customer Satisfaction [presentation] |
Francesco Vivarelli |
source |
35 |
| SEUGI |
2003 |
Sustainable Corporate Performance in an uncertain Energy & Utility world with SAS Analytical Intelligence [presentation] |
Horia Orenstein |
source |
20 |
| PharmaSUG |
2003 |
Application of Some Advanced PROC SQL Features in Clinical Trial Programming |
Changhong Shi |
|
6 |
| PharmaSUG |
2003 |
Datamapper: A Documentation Generator for SAS Metadata |
Lei Zhang |
|
8 |
| PharmaSUG |
2003 |
Macros Used to Create Descriptive Statistic Summary Table of Neurotoxicity Scores |
Jonson C. Jiang |
|
4 |
| PharmaSUG |
2003 |
Parameter estimation for incomplete bivariate longitudinal data in clinical trials |
Naum M. Khutoryansky |
|
6 |
| PharmaSUG |
2003 |
The Use and Abuse of the Program Data Vector |
Jim Johnson |
|
10 |
| PharmaSUG |
2003 |
Why SAS is the Best Place to Put Your Clinical Data |
Steven A. Wilson |
|
10 |
| PharmaSUG |
2003 |
You've Got Data! : Using SAS from Data Receipt to Reporting |
Philip Vecchione |
|
8 |
| SUGI |
2003 |
An Alternative to PROC MI for Large Samples |
David T. Lanning,
Doug Berry |
source |
3 |
| SUGI |
2003 |
Complex Sampling Designs Meet the Flaming Turkey of Glory |
David L. Cassell,
AnnMaria Rousey |
source |
8 |
| SUGI |
2003 |
Developing a Marketing Geographic Segmentation System Using SAS Software |
Allison N. Shubert-Freeman,
Kellie M. Poulin,
Shawn C. Yoder,
Michael R. Morgan,
Jennifer K. Warner |
source |
6 |
| SUGI |
2003 |
Errors, Warnings, and Notes (Oh My) A Practical Guide to Debugging SAS Programs |
Lora D. Delwiche,
Susan J. Slaughter |
source |
10 |
| SUGI |
2003 |
eveloping Custom Analytic Tasks for SAS |
Joe Carter,
Stephen McDaniel,
Mike Porter |
source |
6 |
| SUGI |
2003 |
How SAS Thinks or Why the DATA Step Does What It Does |
Neil H. Howard |
source |
3 |
| SUGI |
2003 |
Java Syntax for SAS Programmers |
Don Boudreaux |
source |
6 |
| SUGI |
2003 |
Modeling Customer Lifetime Value Using Survival Analysis - An Application in the Telecommunications Industry |
Junxiang Lu |
source |
6 |
| SUGI |
2003 |
New Ways and Means to Summarize Files |
Curtis A. Smith |
source |
6 |
| SUGI |
2003 |
SAS/ACCESS to External Databases: Wisdom for the Warehouse User |
Judy Loren |
source |
9 |
| SUGI |
2003 |
Using Formats and Other Techniques to Complete PROC REPORT Tables |
David D. Chapman |
source |
6 |
| SUGI |
2003 |
Why SAS is the Best Place to Put Your Clinical Data |
Steven A. Wilson |
source |
6 |
| MWSUG |
2002 |
A Missing is a Missing is a Missing ... Unless Your're a Project Manager |
Martin Barron |
|
4 |
| MWSUG |
2002 |
Random Coefficient Model for Hazard Analysis Critical Control Points (HACCP) A Pilot Study |
Xinpeng (Tom) Wang,
Yating Sun,
Julie Caswell |
|
3 |
| SCSUG |
2002 |
A FIRST . (but not LAST.) Word on the BY-group Processing |
Christine A. Smiley |
|
9 |
| SCSUG |
2002 |
Beginning Macros |
Dana Rafiee |
|
6 |
| SCSUG |
2002 |
Summarizing and Reorganizing Data |
Jimmy A. DeFoor |
|
7 |
| SCSUG |
2002 |
The Essential Meaning of PROC MEANS: A Beginner's Guide to Data Summarization and Analysis Using PROC MEANS |
Andrew H. Karp |
|
10 |
| NESUG |
2002 |
An Animated Guide: Matching Test & Control Subjects |
Russell M. Lavery,
Glenn Abrahamsen |
|
21 |
| NESUG |
2002 |
An Animated Guide: Speed Merges with Key Indexing, Bitmapping and Hashing |
Paul M. Dorfman,
Russell M. Lavery |
|
10 |
| NESUG |
2002 |
Debugging 101 |
Peter Knapp |
|
10 |
| NESUG |
2002 |
The Essential Meaning of PROC MEANS: A Beginner's Guide to Summarizing Data Using SAS Software |
Gary M. McQuown,
Andrew H. Karp |
|
10 |
| NESUG |
2002 |
To Err Is Human; to Debug, Divine |
Roger Staum |
|
11 |
| NESUG |
2002 |
Using _INFILE_, Substr, Translate, and TranWrd to Manipulate Strings of Text Within the Input Buffer |
Peter Wobus |
|
3 |
| SESUG |
2002 |
Beginning Macros |
Destiny Corporation (a company) |
source |
6 |
| SESUG |
2002 |
DATA Step Essentials |
Neil H. Howard |
source |
10 |
| SESUG |
2002 |
Enhancements to SAS/GRAPH in V9 |
Himesh Patel,
Ravi Devarajan |
source |
10 |
| SESUG |
2002 |
Handling Missing Data with Multiple Imputation Using PROC MI in SAS |
Hossein N. Yarandi |
source |
5 |
| SESUG |
2002 |
Integrating SAS Analytics with Microsoft-based Desktop Applications |
Sam Yamamura |
source |
6 |
| SESUG |
2002 |
Mrs. Clean Tackles Dirty Data |
Janet E. Stuelpner |
source |
3 |
| SESUG |
2002 |
The Essential Meaning of PROC MEANS: A Beginner's Guide to Summarizing Data Using SAS Software |
Andrew H. Karp,
Gary M. McQuown |
source |
10 |
| SESUG |
2002 |
The Magnificent DO |
Paul M. Dorfman |
source |
10 |
| WUSS |
2002 |
Beware of the Leopard: Issues and Uses of PROC SURVEYMEANS |
AnnMaria Rousey |
source |
7 |
| WUSS |
2002 |
Comparison of Several SAS Approaches to Mean Imputation for Time Series Data with Missing Data Points |
Keiko I. Powers |
source |
4 |
| WUSS |
2002 |
DATA Step Internals: Compile and Execute |
Neil H. Howard |
source |
10 |
| WUSS |
2002 |
Doing Big Demographic Segmentation with the Combination of Multivariate Procedures and Macro Facilities |
Charles Sun |
source |
6 |
| WUSS |
2002 |
Enhancements to SAS/GRAPH in V9 |
Himesh Patel |
source |
10 |
| WUSS |
2002 |
Errors, Warnings, and Notes (Oh My) A Practical Guide to Debugging SAS Programs |
Lora D. Delwiche,
Susan J. Slaughter |
source |
10 |
| WUSS |
2002 |
Imputation Strategies for Sexual Orientation Using SAS PROC MI |
Jesse Albert Canchola,
Joe Catania,
Torsten Neilands |
source |
6 |
| WUSS |
2002 |
New Ways and Means to Summarize Files |
Curtis A. Smith |
source |
6 |
| WUSS |
2002 |
Survival Analysis with PHREG: Using MI and MIANALYZE to Accommodate Missing Data |
Arthur L. Carpenter |
source |
6 |
| WUSS |
2002 |
Think You Know Everything About Merge? Here Is Something You May Not Know |
Bharathi Lingala,
Kirsten Naumann Murtagh |
source |
2 |
| WUSS |
2002 |
You've Got Data, Now What? |
Diana D. Suhr |
source |
3 |
| SEUGI |
2002 |
Buidling Credit Scorecards using SAS Enterprise Miner [presentation] |
John Lawrence,
Martin Crossley |
source |
11 |
| SEUGI |
2002 |
Computer management of Human Immunodeficiency Virus Epidemiological Cohorts [presentation] |
Anne Peroz,
Faroudy Boufassa |
source |
39 |
| SEUGI |
2002 |
Implementing BASEL II [presentation] |
Morgens Kristensen |
source |
25 |
| SEUGI |
2002 |
Implementing BASEL II - What to consider [presentation] |
Morgens Kristensen |
source |
26 |
| SEUGI |
2002 |
Improving Bank Profits with Next-Generation Data Mining Techniques [presentation] |
Rafael Alverez Cuesta |
source |
17 |
| PharmaSUG |
2002 |
Date and Time Values in Clinical Trial Data |
George Li |
|
4 |
| PharmaSUG |
2002 |
Efficient Interpolation Calculation |
Julia Z. Zhang |
|
4 |
| PharmaSUG |
2002 |
Imputation of Longitudinal Incomplete Data in Clinical Trials |
Naum M. Khutoryansky |
|
4 |
| PharmaSUG |
2002 |
Macros for Model Building, Summarizing, and Displaying Results of Stepwise Logistic Regressions |
Lori S. Parsons |
|
6 |
| PharmaSUG |
2002 |
Rapid Assessment of Data Set Structures |
Philip Vecchione |
|
4 |
| PharmaSUG |
2002 |
To Err Is Human; To Debug Devine |
Roger Staum |
|
12 |
| SUGI |
2002 |
Bulletproofing Your SAS Results |
Vanessa C. Hayden |
source |
6 |
| SUGI |
2002 |
Data Cleaning 101 |
Ronald P. Cody |
source |
10 |
| SUGI |
2002 |
DATA Step Essentials |
Neil H. Howard |
source |
11 |
| SUGI |
2002 |
Enhancements to SAS/GRAPH in V9 |
Himesh Patel |
source |
|
| SUGI |
2002 |
Fast and Easy Ways to Annoy a Statistician: The Sharing and Presentation of Data Between a SAS Programmer and a Statistician |
Richard M. Mitchell |
source |
10 |
| SUGI |
2002 |
Hashing Rehashed |
Paul M. Dorfman,
Gregg P. Snell |
source |
9 |
| SUGI |
2002 |
Macros for Model Building, Summarizing, and Displaying Results of Stepwise Logistic Regressions |
Lori S. Parsons |
source |
6 |
| SUGI |
2002 |
Online Docs and Help: Tips for Old Dogs and New Pups |
Michael D. Rhoads,
Deb Cassidy |
source |
9 |
| SUGI |
2002 |
Passing the Buck |
David H. Johnson |
source |
7 |
| SUGI |
2002 |
Predicting Customer Churn in the Telecommunications Industry - An Application of Survival Analysis Modeling Using SAS |
Junxiang Lu |
source |
6 |
| SUGI |
2002 |
Seeing Red: Tips for Debugging the SAS DATA Step |
Michael J. Yee |
source |
4 |
| SUGI |
2002 |
SEM for Health, Business, and Education |
Diana D. Suhr |
source |
8 |
| SUGI |
2002 |
Some Uses (and Handy Abuses) of PROC TRANSPOSE |
Ralph W. Leighton |
source |
10 |
| SUGI |
2002 |
Stop Madly Merging: PROC PRINT to the Rescue! |
Catherine A. Lindsey |
source |
2 |
| SUGI |
2002 |
Taking Advantage of Missing Values in PROC SQL |
Ya Huang |
source |
3 |
| SUGI |
2002 |
The Heuristic Program |
John R. Gerlach |
source |
4 |
| SUGI |
2002 |
To Err Is Human; to Debug, Divine |
Roger Staum |
source |
11 |
| SUGI |
2002 |
Unit-of-Analysis Programming |
Vanessa C. Hayden |
source |
6 |
| MWSUG |
2001 |
Date & Time Values in Clinical Trial Data |
George Li,
John Morrill |
|
4 |
| MWSUG |
2001 |
Modeling Lifetime Value in the Insurance Industry |
C. Olivia Parr Rud |
|
9 |
| MWSUG |
2001 |
The Essential Meaning of PROC MEANS: A Beginner's Guide to Data Summarization Using the SAS System |
Andrew H. Karp |
|
10 |
| NESUG |
2001 |
Array Tutorial (2) $ Beginning Intermediate; |
Marge Scerbo |
|
7 |
| NESUG |
2001 |
Fast and Easy Ways to Annoy a Statistician: The Sharing and Presentation of Data Between a SAS Programmer and a Statistician |
Richard M. Mitchell |
|
10 |
| NESUG |
2001 |
How to Represent Missing Data: Special Missing Values vs. 999999999 |
Quentin McMullen |
|
4 |
| NESUG |
2001 |
INTERPOLATION CALCULATION MADE EZ |
Julia Z. Zhang,
David C. Chen |
|
4 |
| NESUG |
2001 |
Introduction to the SAS Macro Language |
Destiny Corporation (a company) |
|
6 |
| NESUG |
2001 |
Migrating Your SAS ODBC Application To ADO |
David Shamlin |
|
7 |
| NESUG |
2001 |
Mrs. Clean Tackles Dirty Data |
Janet E. Stuelpner |
|
3 |
| NESUG |
2001 |
SAS Program Efficiency for Beginners |
Bruce F. Gilsen |
|
10 |
| NESUG |
2001 |
Some Uses (and Handy Abuses) of PROC TRANSPOSE |
Ralph W. Leighton |
|
10 |
| NESUG |
2001 |
The Dirty Dozen: Twelve Common Programming Mistakes |
Bob Virgile |
|
5 |
| NESUG |
2001 |
Top-Down Programming with SAS Macros |
Edward Heaton |
|
9 |
| VIEWS |
2001 |
Data Mining and Statistical Models in Marketing Campaigns of BT Retail |
Francesco Vivarelli,
Martyn Johnson |
|
9 |
| VIEWS |
2001 |
Enterprise Guide - The New Features in Version 1.2 [presentation] |
Jeremy Rankcom |
|
16 |
| VIEWS |
2001 |
The Version 8 Applied Statistician |
David Shannon |
|
15 |
| WUSS |
2001 |
Basic Macro Processing |
Destiny Corporation (a company) |
source |
6 |
| SESUG |
2001 |
Basic Macro Processing |
Destiny Corporation (a company) |
source |
6 |
| SESUG |
2001 |
Direct Addressing Techniques of Table Look-Up Part 1. Key-Indexing - Bitmapping Part 2. Hashing |
Paul M. Dorfman |
source |
10 |
| SESUG |
2001 |
Elegant Tables: Dressing Up Your TABULATE Results |
Lauren E. Haworth |
source |
9 |
| SESUG |
2001 |
Manipulating Data: Elements of the DATA Step Language |
Paul M. Dorfman |
source |
10 |
| SESUG |
2001 |
Top-Down Programming with SAS Macros |
Edward Heaton |
source |
9 |
| SESUG |
2001 |
Understanding and Using Functions |
Frank C. DiIorio |
source |
8 |
| SESUG |
2001 |
Using SAS to Create Presentation Quality Spreadsheets in Excel |
Joyce R. Hartley |
source |
5 |
| SCSUG |
2001 |
Basic Macro Processing |
Destiny Corporation (a company) |
|
6 |
| SCSUG |
2001 |
Direct Addressing Techniques of Table Look-Up Part 1. Key-Indexing - Bitmapping Part 2. Hashing |
Paul M. Dorfman |
|
10 |
| SCSUG |
2001 |
Manipulating Data: Elements of the DATA Step Language |
Paul M. Dorfman |
|
10 |
| SCSUG |
2001 |
Top-Down Programming with SAS Macros |
Edward Heaton |
|
9 |
| SCSUG |
2001 |
Understanding and Using Functions |
Frank C. DiIorio |
|
8 |
| SEUGI |
2001 |
Fraud Detection in Healthcare - MBF's successful approach through SAS South Pacific Data Mining Discovery Centre [presentation] |
Jolie Reichel |
source |
14 |
| SEUGI |
2001 |
How to approach a segmentation project for ten million customers with SAS Enterprise Miner [presentation] |
Luis Miguel Muruzabal |
source |
26 |
| SEUGI |
2001 |
SAS and CFMS Integration - Attacking Fraud in Next Generation Networks [presentation] |
Augusto Argento |
source |
35 |
| PharmaSUG |
2001 |
Imputation Techniques Using SAS Software For Incomplete Data In Diabetes Clinical Trials |
Naum M. Khutoryansky,
Won-Chin Huang |
|
4 |
| SUGI |
2001 |
An Introduction to Computing and Interpreting Cronbach Coefficient Alpha in SAS |
Chong Ho Yu |
source |
6 |
| SUGI |
2001 |
Array Tutorial (2) $ Beginning Intermediate; |
Marge Scerbo |
source |
7 |
| SUGI |
2001 |
Data Cleaning 101 |
Ronald P. Cody |
source |
10 |
| SUGI |
2001 |
Efficient Statistical Programming? - Let SAS Do the Work |
Keiko I. Powers |
source |
4 |
| SUGI |
2001 |
Fast and Easy Ways to Annoy a Statistician: The Sharing and Presentation of Data Between a SAS Programmer and a Statistician |
Richard M. Mitchell |
source |
10 |
| SUGI |
2001 |
Hip, Hip, ARRAY... Duplicating and Modifying Records With an ARRAY |
Jeanina M. Worden,
Lauren L. Shinaberry |
source |
3 |
| SUGI |
2001 |
Migrating Your SAS ODBC Application To ADO |
David Shamlin |
source |
7 |
| SUGI |
2001 |
Modeling Lifetime Value in the Insurance Industry |
C. Olivia Parr Rud |
source |
8 |
| SUGI |
2001 |
Table Look-Up by Direct Addressing: Key-Indexing - Bitmapping - Hashing |
Paul M. Dorfman |
source |
10 |
| SUGI |
2001 |
The Essential Meaning of PROC MEANS: A Beginner's Guide to Summarizing Data in SAS |
Andrew H. Karp |
source |
10 |
| SUGI |
2001 |
Three for the Show - Three Objects for Presenting Categorized Data |
Thomas A. Miron |
source |
7 |
| SCSUG |
2000 |
Basic Macro Processing |
Destiny Corporation (a company) |
|
6 |
| SCSUG |
2000 |
Data Cleaning 101 |
Ronald P. Cody |
|
13 |
| SCSUG |
2000 |
Repeated-Measures Analysis and PROC MIXED |
Shana Smith,
Torsten B. Neilands |
|
17 |
| SCSUG |
2000 |
The Use of PROC MIXED for Analyzing Cohort-Sequential Designs |
C. Nathan Marti |
|
11 |
| SESUG |
2000 |
Data Mining: An Overview of Methods and Technologies for Increasing Profits in Direct Marketing |
C. Olivia Parr Rud |
source |
7 |
| SESUG |
2000 |
Data Structure Determines Programming Effort |
H. Ian Whitlock |
source |
9 |
| SESUG |
2000 |
SAS Macros Are the Cure for Quality Control Pains Implementing A Macro Driven Evaluation of Survey Data for Quality Control |
Gary M. McQuown |
source |
7 |
| SESUG |
2000 |
Table Lookup via Direct Addressing: Key-Indexing, Bitmapping, Hashing |
Paul M. Dorfman |
source |
11 |
| SESUG |
2000 |
Underlying Construct Analysis (UCA): Where Individual Systems End and Human Systems Begin |
Alvin L. Killough |
source |
8 |
| NESUG |
2000 |
Arrays: In and Out and All About |
Marge Scerbo |
|
7 |
| NESUG |
2000 |
Avoiding Mayhem in the New Millennium: Working with Missing Data |
JoAnn R. Matthews |
|
10 |
| NESUG |
2000 |
Basic Macro Processing |
Destiny Corporation (a company) |
|
6 |
| NESUG |
2000 |
Clean-up, Comments and Code - Making it Maintainable |
Clay Martin,
Lori Martin |
|
7 |
| NESUG |
2000 |
Data Structure Determines Programming Effort |
H. Ian Whitlock |
|
9 |
| NESUG |
2000 |
NPV Modeling for Direct Mail Insurance |
C. Olivia Parr Rud |
|
7 |
| NESUG |
2000 |
Partially Transforming Hierarchical Data Sets for Sequential Processing Using Arrays |
Richard L. Downs Jr.,
Pura A. Perez |
|
8 |
| NESUG |
2000 |
PROC MEANSTM: More than just your average procedure |
Peter R. Welbrock |
|
10 |
| NESUG |
2000 |
SAS Macros are the Cure for Quality Control Pains |
Gary M. McQuown |
|
6 |
| NESUG |
2000 |
SAS Program Efficiency for Beginners |
Bruce F. Gilsen |
|
10 |
| NESUG |
2000 |
Some Uses (and Handy Abuses) of PROC TRANSPOSE |
Ralph W. Leighton |
|
12 |
| NESUG |
2000 |
The INPUT Statement: Where It's @ |
Ronald P. Cody |
|
10 |
| WUSS |
2000 |
Subsetting Files vs. Summarizing Files |
Curtis A. Smith |
source |
6 |
| WUSS |
2000 |
Utilization of SAS Programs in the Business Environment |
Sunil Kumar Gupta,
Charles E. Shipp |
source |
5 |
| MWSUG |
2000 |
Advanced DATA Step Techniques |
Neil H. Howard |
|
10 |
| MWSUG |
2000 |
Managing Independent Consultants - An Oxymoron? |
Neil H. Howard,
Michelle Gayari |
|
9 |
| MWSUG |
2000 |
Merges and Joins |
Timothy J. Harrington |
|
6 |
| SEUGI |
2000 |
Churn in a Prepaid Cellular Market [presentation] |
Marcelle Georgiev,
Mohan Namboodiri |
source |
23 |
| SEUGI |
2000 |
Creating validated standard output using the System Development Life Cycle (SDLC): A practical approach with the Biometrics Department of Solvay Pharmaceuticals [presentation] |
Saskia Janssen |
source |
37 |
| SEUGI |
2000 |
Development of Micro-Aggregation System for Confidential Data [presentation] |
L. Bechet,
P. Loosveldt,
T. Le Tri |
source |
44 |
| SEUGI |
2000 |
E.S.S.E. Editing Systems Standard Evaluation - Software Framework [presentation] |
A. Manzari,
G. Della Rocca,
M. Di Zao,
O. Luzi |
source |
20 |
| SEUGI |
2000 |
Forecasting Bad Debt by ARIMA Models with Multiple Transfer Functions using a Selection Process for many Candidate Variables [presentation] |
Alastair Jones,
Derek Hull,
John Adam |
source |
18 |
| SEUGI |
2000 |
Having the Proce Report Improvements Suggested in the 2000 SASware Ballot NOW! [presentation] |
David R. Trenery |
source |
67 |
| SEUGI |
2000 |
Moving to SAS Version 8 with Oracle |
Clare Somerville,
Colin J. Harris |
source |
8 |
| SEUGI |
2000 |
SAS/Knowledge Solution for R/3 and CFO-Vision [presentation] |
Antje Mohrmann,
Soren Ingstrup Hansen |
source |
21 |
| SEUGI |
2000 |
Web Mining [presentation] |
Henrik Wagner |
source |
26 |
| PharmaSUG |
2000 |
Managing Independent Consultants - An Oxymoron? |
Neil H. Howard,
Michelle Gayari |
|
10 |
| PharmaSUG |
2000 |
The disadvantages of graphing "LOCF" data and a way of incorporating Discontinuation information graphically using PROC GREPLAY |
Sangeeta Bhattacharya |
|
4 |
| SUGI |
2000 |
Advanced DATA Step Techniques |
Neil H. Howard |
source |
10 |
| SUGI |
2000 |
Arrays: In and Out and All About |
Marge Scerbo |
source |
6 |
| SUGI |
2000 |
Avoiding Mayhem in the New Millennium: Working with Missing Data |
JoAnn R. Matthews |
source |
10 |
| SUGI |
2000 |
Building A Better Data Entry Application Using PROC FSEDIT |
Derek P. Morgan,
Michael A. Province |
source |
10 |
| SUGI |
2000 |
Correcting for Missing Discrete Responses in Business Surveys |
Lawrence C. Marsh |
source |
10 |
| SUGI |
2000 |
Design and Analysis of Cancer Control Studies via the SAS System |
Jeff A. Sloan,
Charles L. Loprinzi,
Paul J. Novotny |
source |
10 |
| SUGI |
2000 |
Implementing Public Sector Performance Management: A Balanced Scorecard Approach |
Kristin Nauta |
source |
4 |
| SUGI |
2000 |
Merges and Joins |
Timothy J. Harrington |
source |
6 |
| SUGI |
2000 |
Multiple Imputation for Missing Data: Concepts and New Development |
Yang Yuan |
source |
10 |
| SUGI |
2000 |
PROC MEANS: More Than Just Your Average Procedure |
Peter R. Welbrock |
source |
10 |
| SUGI |
2000 |
The INPUT Statement: Where It's @ |
Ronald P. Cody |
source |
10 |
| SUGI |
2000 |
Transforming SAS Data Sets Using Arrays |
Ronald P. Cody |
source |
6 |
| SCSUG |
1999 |
Advanced Features of PROC TABULATE |
Thomas J. Winn Jr. |
|
6 |
| SESUG |
1999 |
Analysis of Longitudinal Data: Comparison between PROC GLM and PROC MIXED |
Maribeth H. Johnson |
source |
6 |
| SESUG |
1999 |
Data Mining: An Overview of Methods and Technologies for Increasing Profits in Direct Marketing |
C. Olivia Parr Rud |
source |
7 |
| SESUG |
1999 |
Do We Know How to Function? |
Janet E. Stuelpner |
source |
7 |
| SESUG |
1999 |
EVERYTHING IN ONE RECORD: CREATING ONE-LINERS WITH MERGES AND RETAINS |
Sharon W. Stroud |
source |
3 |
| SESUG |
1999 |
Forecasting Army Officer Attrition with Logistic Regression Using The SAS System |
Douglas L. McAllaster |
source |
10 |
| SESUG |
1999 |
FUZZY MERGES: Examples and Techniques |
Malachy J. Foley |
source |
10 |
| SESUG |
1999 |
Hiring the Best SAS Programmers |
Neil H. Howard |
source |
10 |
| SESUG |
1999 |
Macro Internals for the User - Developer's Overview |
Susan M. O'Connor |
source |
14 |
| SESUG |
1999 |
Statistical Presentations Using the Power of PROC TABULATE |
C. Olivia Parr Rud |
source |
8 |
| SESUG |
1999 |
Those Missing Values in Questionnaires |
John R. Gerlach,
Plymouth Meeting |
source |
6 |
| WUSS |
1999 |
An Everyday Guide to Versions 7 and 8 of the SAS System |
Susan J. Slaughter,
Lora D. Delwiche |
source |
10 |
| WUSS |
1999 |
Data Mining: An Overview of Methods and Technologies for Increasing Profits in Direct Marketing |
C. Olivia Parr Rud |
source |
6 |
| WUSS |
1999 |
Using PROC EXPAND with Time Series Data |
Andrew H. Karp |
source |
4 |
| NESUG |
1999 |
Data Cleaning 101 |
Ronald P. Cody |
|
10 |
| NESUG |
1999 |
Data Mining: An Overview of Methods and Technologies for Increasing Profits in Direct Marketing |
C. Olivia Parr Rud |
|
7 |
| NESUG |
1999 |
How Should I Combine My Data, Is The Question |
Stephen Thompson,
Asha Sharma |
|
6 |
| NESUG |
1999 |
Robust Programming Techniques in the SAS System |
Alice M. Cheng |
|
10 |
| NESUG |
1999 |
Some Uses (and Handy Abuses) of PROC TRANSPOSE |
Ralph W. Leighton |
|
10 |
| NESUG |
1999 |
The RETAIN Statement: One Window into the SAS Data Step |
Paul Gorrell |
|
6 |
| NESUG |
1999 |
Those Missing Values in Questionnaires |
John R. Gerlach,
Cindy Garra |
|
6 |
| MWSUG |
1999 |
Updating Some Tips and Techniques to Version 7 |
Deb Cassidy |
|
10 |
| PharmaSUG |
1999 |
MISSING VALUES: Everything You Ever Wanted to Know |
Malachy J. Foley |
|
5 |
| SUGI |
1999 |
A Simple Framework for Sequentially Processing Hierarchical Data Sets for Large Surveys |
Richard L. Downs Jr.,
Pura A. Perez |
source |
6 |
| SUGI |
1999 |
Advanced DATA Step Topics |
Neil H. Howard |
source |
10 |
| SUGI |
1999 |
Advanced Features of PROC TABULATE |
Thomas J. Winn Jr. |
source |
6 |
| SUGI |
1999 |
An Everyday Guide to Version 7 of the SAS System |
Susan J. Slaughter,
Lora D. Delwiche |
source |
8 |
| SUGI |
1999 |
Building A Better Data Entry Application Using PROC FSEDIT |
Derek P. Morgan,
Michael A. Province |
source |
10 |
| SUGI |
1999 |
Changing the Shape of Your Data: PROC TRANSPOSE vs. Arrays |
Bob Virgile |
source |
6 |
| SUGI |
1999 |
Data Mining: An Overview of Methods and Techniques for Increasing Profits in Direct Marketing |
C. Olivia Parr Rud |
source |
7 |
| SUGI |
1999 |
FUZZY MERGES: Examples and Techniques |
Malachy J. Foley |
source |
10 |
| SUGI |
1999 |
How and When to Use WHERE |
J. Meimei Ma,
Sandra D. Schlotzhauer |
source |
8 |
| SUGI |
1999 |
Introduction to SAS Functions |
Neil H. Howard |
source |
7 |
| SUGI |
1999 |
Outta Space with SAS Software? |
Philip R. Holland |
source |
5 |
| SUGI |
1999 |
Performance Enhancements to PROC SQL in Version 7 of the SAS System |
Lewis Church Jr. |
source |
6 |
| SUGI |
1999 |
Reporting Multidimensional Data on the Web using SAS/GRAPH and SAS/IntrNet Software |
Edward A. Tasch |
source |
7 |
| SUGI |
1999 |
Statistical Presentations Using the Power of PROC TABULATE |
C. Olivia Parr Rud |
source |
7 |
| SUGI |
1999 |
The Effect of Missing Data on Repeated Measures Models |
Maribeth H. Johnson |
source |
6 |
| SUGI |
1999 |
The SAS Debugging Primer |
Frank C. DiIorio |
source |
5 |
| SUGI |
1999 |
Transforming SAS Data Sets Using Arrays |
Ronald P. Cody |
source |
6 |
| SUGI |
1999 |
Two Macros to Compute the Median for a List of Variables |
Fan Xu |
source |
4 |
| MWSUG |
1998 |
Less Well Known Features of SAS PROC SQL |
Timothy J. Harrington |
|
10 |
| SCSUG |
1998 |
Creating SAS Reports from Data Collected Via the Internet Using Microsoft FrontPage 98 Hosted on a PC Running the Microsoft Personal Web Server |
David Workman,
Richard F. Pless |
|
10 |
| SCSUG |
1998 |
Introduction to Using PROC TABULATE |
Thomas J. Winn Jr. |
|
11 |
| WUSS |
1998 |
A Procedure to Automatically Update Web Pages with the SAS System and a Web Agent |
Loan X. Nguyen |
source |
4 |
| WUSS |
1998 |
An Innovative Approach of Analyzing Longitudinal Data ith Various Patterns of Missing Data |
Shiao-Ping Lu |
source |
3 |
| WUSS |
1998 |
Tapping the Power of PROC TABULATE for Analysis Reports Clients Understand |
Ed Jensen |
source |
10 |
| WUSS |
1998 |
The Elements of SAS Programming Style |
Frank C. DiIorio |
source |
5 |
| NESUG |
1998 |
Changing the Shape of Your Data: PROC TRANSPOSE vs. Arrays |
Bob Virgile |
|
6 |
| NESUG |
1998 |
Data Mining: An Overview of Methods and Techniques for Increasing Profits in Direct Marketing |
C. Olivia Parr Rud |
|
8 |
| NESUG |
1998 |
Debugging 101 |
Peter Knapp |
|
10 |
| NESUG |
1998 |
Handling Missing Values in the SQL Procedure |
Danbo Yi |
|
8 |
| NESUG |
1998 |
Indexed Table Lookup vs Multiple Data Sets |
Douglas L. McAllaster |
|
4 |
| NESUG |
1998 |
Internet Based Data Collection for Analysis with SAS |
Robert S. Workman |
|
3 |
| NESUG |
1998 |
Macro for computing LOCF: Comparison of results with PROC MIXED |
Patricia R. Houck |
|
2 |
| NESUG |
1998 |
SAS Program Efficiency for Beginners |
Bruce F. Gilsen |
|
10 |
| NESUG |
1998 |
Statistical Presentations Using the Power of PROC TABULATE |
C. Olivia Parr Rud |
|
8 |
| NESUG |
1998 |
The INPUT Statement: Where It's @ |
Ronald P. Cody |
|
10 |
| SESUG |
1998 |
A Simple Framework for Sequentially Processing Hierarchical Data Sets for Large Surveys |
Richard L. Downs Jr.,
Pura A. Perez |
|
10 |
| SESUG |
1998 |
An Applicatio.n of Cluster Analysis in the Financial Services Industry |
Satrsh Nargundkar,
Strategic Decision |
|
6 |
| SESUG |
1998 |
Changing the Shape of Your Data: PROC TRANSPOSE vs. Arrays |
Bob Virgile |
|
6 |
| SESUG |
1998 |
Data Dictionary Approaches to Edit and Validate Survey Data |
Naoko S. Steams |
|
4 |
| SESUG |
1998 |
Data Mining: An Overview of Methods and Technologies for Increasing Profits in Direct Marketing |
C. Olivia Parr Rud |
|
7 |
| SESUG |
1998 |
Formats as a Programming Tool |
John R. Gerlach |
|
7 |
| SESUG |
1998 |
MATCH-MERGING: 27 Plus Traps and How to Avoid Them |
Malachy J. Foley |
|
10 |
| SESUG |
1998 |
SERENDIPITY Indexed Table Look-up Against Multiple Data Sets |
Douglas L. McAllaster |
|
4 |
| SESUG |
1998 |
The Effect of Missing Data on Repeated Measures Models |
Maribeth H. Johnson |
|
5 |
| SESUG |
1998 |
The Replication Method for Estimating Sampling Errors, Principles, and Software |
David Morganstein,
J. Michael Brick |
|
5 |
| PNWSUG |
1998 |
Beginning Macros |
Dana Rafiee |
|
6 |
| PNWSUG |
1998 |
Tapping the Power of PROC TABULATE for Analysis Reports Clients Understand |
Edward Jensen |
|
10 |
| PNWSUG |
1998 |
Using PROC EXPAND with Time Series Data |
Andrew H. Karp |
|
4 |
| SEUGI |
1998 |
GLM and MIXED procedures to treat longitudinal data |
Sandrine Laguerre |
source |
9 |
| SUGI |
1998 |
A Revolution in Data Analysis: How New, Very Powerful, Easy to Use, Graphical Data Analysis Tools and Techniques Can Empower Even Novice Subject Matter Specialists |
Dave L. DesJardins |
source |
8 |
| SUGI |
1998 |
Finding the Solution to Data Mining: A Map of the Features and Components of SAS Enterprise Miner Software Table of Contents |
John C. Brocklebank,
Mark Brown |
source |
24 |
| SUGI |
1998 |
Hot-Deck Imputation with SAS Arrays and Macros for Large Surveys |
John G. Stiller,
Donald R. Dalzell |
source |
6 |
| SUGI |
1998 |
MATCH-MERGIN G : 20 Some T raps and How to Avoid Them |
Malachy J. Foley |
source |
21 |
| SUGI |
1998 |
Mining the Data Mart: A Case Study with Stock Market Data |
James A. Cox,
Tonya L. Etchison |
source |
11 |
| SUGI |
1998 |
Multivariate Analysis Using the MIXED Procedure |
S. Paul Wright |
source |
5 |
| SUGI |
1998 |
On Data Transfer |
Aileen L. Yam |
source |
10 |
| SUGI |
1998 |
Overall Part Number Item |
Rank Rank |
source |
19 |
| SUGI |
1998 |
Reporting Results of Multiple Logistic Regression Models Depending on the A vailability of Data |
Richard M. Mitchell |
source |
5 |
| SUGI |
1998 |
SAS Program Efficiency for Beginners |
Bruce F. Gilsen |
source |
9 |
| SUGI |
1998 |
Statistical Presentations Using the Power of PROC TABULATE |
C. Olivia Parr Rud |
source |
7 |
| SUGI |
1998 |
Taking the DrudgerYout of Data Checking: Automatic Data Validation Using FORMATS to Validate Data, PROC DATASETS to Drive the Process and MACROS to Hang It All T ogether |
David R. Trenery |
source |
5 |
| SUGI |
1998 |
Taking the Mystery Out of SAS Macro When Using CALL SYMPUT |
K. Larry Landers,
Monique Bryher |
source |
6 |
| SUGI |
1998 |
The Effect of Missing Data on Sample Sizes for Repeated Measures Models |
Maribeth H. Johnson,
Peter P. Davis |
source |
5 |
| SUGI |
1998 |
The INPUT Statement: Where It's @ |
Ronald P. Cody |
source |
10 |
| SUGI |
1998 |
The SAS Multidimensional Database Procedure |
Timothy J. Harrington |
source |
7 |
| SCSUG |
1997 |
A Macro to Account for Missing Data in a Table |
Margie Bell,
Stephen Black,
Jennifer K. Fulton |
|
10 |
| SCSUG |
1997 |
The INPUT Statement: Where is it's @ |
Ronald P. Cody |
|
10 |
| PNWSUG |
1997 |
Debugging your SAS Output |
Lauren E. Haworth |
|
7 |
| PNWSUG |
1997 |
Using Excel with SAS to Combine Customer Survey Data Sets |
Cyndi V. Thompson,
Mark E. Thompson |
|
5 |
| WUSS |
1997 |
A Data Management System for Use in Smaller Biotech Companies |
Ellen Krantz |
source |
5 |
| WUSS |
1997 |
Taking the Mystery Out of SAS Macro When Using CALL SYMPUT |
K. Larry Landers,
Monique Bryher |
source |
6 |
| NESUG |
1997 |
FREQ-OUT: An Applied Presentation of the Options and Output of the FREQ Procedure |
Pamela Landsman |
|
6 |
| NESUG |
1997 |
Hot-Deck Imputation with SAS Arrays and Macros for Large Surveys |
John Stiller |
|
6 |
| NESUG |
1997 |
Missing Value Imputation Methods for Large Databases |
Leonardo Auslender |
|
8 |
| NESUG |
1997 |
The INPUT Statement: Where It's @ |
Ronald P. Cody |
|
10 |
| NESUG |
1997 |
Time Series Magic: Smoothing, Interpolating, Expanding and Collapsing Time Series Data With PROC EXPAND |
Andrew H. Karp |
|
5 |
| MWSUG |
1997 |
A Macro for Efficient and Flexible Data Smoothing |
John Cucka |
|
7 |
| MWSUG |
1997 |
Bits to Bytes --More Efficient Memory Utilization |
Brian L. Bournival |
|
3 |
| MWSUG |
1997 |
Macro Facility Enhancements for Release 6.09E and Release 6.11 |
Jeffrey A. Polzin,
Susan M. O'Connor |
|
11 |
| SESUG |
1997 |
Analysis of Repeated Measures and Longitudinal Data |
Jane Pendergast |
|
8 |
| SESUG |
1997 |
Learning About Your Data: Tips and Techniques for Looking at Large Files |
Sandra D. Schlotzhauer |
|
10 |
| SESUG |
1997 |
Statistical Presentations Using the Power of PROC TABULATE |
C. Olivia Parr Rud |
|
8 |
| SESUG |
1997 |
The Right Approach to Learning PROC TABULATE |
Bob Virgile |
|
7 |
| SEUGI |
1997 |
A data management system for use in the pharmaceutical and biotechnology Industries |
Ellen Krantz |
source |
12 |
| SEUGI |
1997 |
Neural networks for business benefit |
Michael Kerin |
source |
20 |
| PharmaSUG |
1997 |
How Do I Take the Mean of an Apple and an Orange? Techniques for Integrating Data from Multiple Studies |
Linda Collins |
|
8 |
| PharmaSUG |
1997 |
More About Missing Character Data |
Hany Aboutaleb |
|
2 |
| SUGI |
1997 |
Characterization of Variance in Medical Diagnostics: The Analysis of "Usual" Data |
John A. Wass |
source |
5 |
| SUGI |
1997 |
Creating an EIS in Less Than 2 Hours |
Deb Cassidy |
source |
7 |
| SUGI |
1997 |
Developing Risk Adjustment Techniques Using the SAS System for Assessing Health Care Quality in the IMSystem |
Yanchun Xu,
Andrius Kubilius |
source |
5 |
| SUGI |
1997 |
Errors, Warnings, and Notes (Oh My) A Practical Guide to Debugging SAS Programs |
Lora D. Delwiche,
Susan J. Slaughter |
source |
10 |
| SUGI |
1997 |
Estimation via EM-Algorithm in a Bivariate Random Effects Model |
Amrik Shah,
Kirk A. Easley |
source |
8 |
| SUGI |
1997 |
Learning About Your Data: Tips and Techniques for Looking at Large Files |
Sandra D. Schlotzhauer,
Bob Anschuetz |
source |
|
| SUGI |
1997 |
Reading and Writing Character Data |
Craig Dickstein,
Marge Scerbo |
source |
8 |
| SUGI |
1997 |
SAS Program Efficiency for Beginners |
Bruce F. Gilsen |
source |
10 |
| SUGI |
1997 |
Using Survey Data: Tips to Pick Up Speed on the Road to Analysis |
Cynthia L. Williamson,
Suzanne D. Kreutzer |
source |
6 |
| SESUG |
1996 |
MATCH-MERGING: Techniques, Tricks, and Traps |
Malachy J. Foley |
|
8 |
| SESUG |
1996 |
The Elements of SASe Programming Style |
Frank C. Dilorio,
Jean Cocteau |
|
5 |
| NESUG |
1996 |
Poor Mans's Missing Value Recovery Methods |
Leonardo Auslender |
|
10 |
| NESUG |
1996 |
Reading and Writing Character Data |
Craig Dickstein,
Marge Scerbo |
|
8 |
| NESUG |
1996 |
SAS ARRAYS: A Basic Tutorial |
Bruce F. Gilsen |
|
9 |
| NESUG |
1996 |
SAS Program Efficiency for Beginners |
Bruce F. Gilsen |
|
10 |
| NESUG |
1996 |
The Dirty Dozen: Twelve Common Programming Mistakes |
Bob Virgile |
|
6 |
| MWSUG |
1996 |
Testing for and Correcting for Missing Data in Survey Responses: A Survey of Downtown Business in Minneapolis |
Shelly S. Baxter,
Lawrence C. Marsh |
|
10 |
| WUSS |
1996 |
Dressing for Success with SAS Software Functions |
Faith Reneé Sloan |
source |
5 |
| WUSS |
1996 |
Using PROC EXPAND with Time Series Data |
Andrew H. Karp |
source |
4 |
| WUSS |
1996 |
Using Survey Data: Tips to Pick Up Speed on the Road to Analysis |
Cynthia L. Williamson,
Suzanne D. Kreutzer |
source |
6 |
| WUSS |
1996 |
What's Bugging You?: A Guide to Debugging SAS Programs |
Susan J. Slaughter,
Lora D. Delwiche |
source |
10 |
| SUGI |
1996 |
Advanced DATA Step Topics and Tips |
Neil H. Howard,
Cynthia L. Zender |
source |
|
| SUGI |
1996 |
Advanced Topics in PROC REPORT |
Christopher Yindra |
source |
10 |
| SUGI |
1996 |
Data Transfer between SAS Software and Other PC Software |
J. Michael Miscisin |
source |
5 |
| SUGI |
1996 |
Imputation of Data Missing Due to Truncation --- A SAS Macro for Maximum Likelihood Estimates Using Symmetry Property |
Zhengyu Wang,
ViswanaThan Ramakrishnan,
John M. Davis |
source |
6 |
| SUGI |
1996 |
It'S Not a Bug, It's a Feature!! |
Neil H. Howard,
Linda Williams Pickle,
James B. Pearson Jr. |
source |
|
| SUGI |
1996 |
Loading a Data Warehouse with Legacy Data |
Clive Cooper,
Clare Somerville |
source |
6 |
| SUGI |
1996 |
MACRO Facifity Enhancements for Release 6.09E and Release 6.11 |
Jeffrey A. Polzin,
Susan M. O'Connor |
source |
11 |
| SUGI |
1996 |
Reading and Writing Character Data |
Craig Dickstein,
Marge Scerbo |
source |
8 |
| SUGI |
1996 |
SUM Enchanted Procedure: The Advanced Power of PROC SUMMARY |
David E. Beam |
source |
6 |
| SUGI |
1996 |
The Dirty Dozen: Twelve Common Programming Mistakes |
Robert Virgile |
source |
6 |
| SUGI |
1996 |
The Elements of SAS Programming Style |
Frank C. Dilorio |
source |
5 |
| SCSUG |
1995 |
Advanced Analysis of Stock Prices Using the CRSP Tapes and SAS: Protocols for Both Academic Research and Classroom Use |
Glenn Millard,
Garland Simmons |
|
20 |
| SCSUG |
1995 |
Data Entry and Data Transform Errors -- What They Are, How to Prevent Them, and How to Correct Them |
Iris McPherson |
|
10 |
| MWSUG |
1995 |
A Comparison of the MODULE Routines with S-PLUS Extensibility |
Richard D. Langston |
|
4 |
| MWSUG |
1995 |
Discussion of the Comparison of GLM and MIXED Procedures for a Repeated Measures Design: One Within and One Between Subjects Factor |
Michelle Secic |
|
6 |
| MWSUG |
1995 |
Quality Improvement in SAS Coding: Reducing Defects |
Linda M. Quinn |
|
8 |
| MWSUG |
1995 |
Simulation and Bootstrapping in the SAS System: A tutorial using PROC IML |
Paul A. Thompson |
|
10 |
| NESUG |
1995 |
A Generic Data Dictionary for SAS Applications |
Ralph W. Leighton |
|
11 |
| NESUG |
1995 |
Advanced Features of PROC REPORT in Batch Mode |
Christopher Yindra |
|
10 |
| NESUG |
1995 |
Data Reduction: An Introduction to Principal Components Analysis |
Jennifer L. Caveny,
James F. Murray |
|
10 |
| NESUG |
1995 |
Dynamic Data Selection Lists in SAS/AF Programming Entries |
John M. Rinehart |
|
7 |
| NESUG |
1995 |
Getting the Most from the SAS LOG |
Robert G. Hall |
|
4 |
| NESUG |
1995 |
HOTROD: An Intelligent Hotdeck Imputation System |
Eugene M. Burns,
Jay Olsen |
|
8 |
| NESUG |
1995 |
Imputing Missing Values to Survey Data: Three SAS Algorithms |
Roald Euller |
|
2 |
| NESUG |
1995 |
Managing Large Databases With Customized SAS Windows |
Ronald W. Matheny |
|
10 |
| NESUG |
1995 |
SAS, FileMaker Pro, and Microsoft Access for Clinical Study Data Management |
Karen G. Malley |
|
9 |
| NESUG |
1995 |
Some Clever Things to Do with User Defined INFORMATS |
Ronald P. Cody |
|
6 |
| NESUG |
1995 |
Using PROC EXPAND with Time Series Data |
Andrew H. Karp |
|
4 |
| NESUG |
1995 |
What We Learned At NESUG: An Application of Lesson Learned at NESUG '91, '92, '93 & '94 |
Charles A. DePascale,
Patricia Cole |
|
4 |
| WUSS |
1995 |
Introduction to PROC SQL as a Data Summary Tool |
Aileen L Yarn |
source |
5 |
| SESUG |
1995 |
Applications Development Building Quality into the SAS System |
Frank Lassiter |
|
5 |
| SESUG |
1995 |
But, It Runs |
Grace J. Lossman |
|
6 |
| SESUG |
1995 |
LAGging Questions |
Debbie Hoppe,
John Alden |
|
2 |
| SESUG |
1995 |
The Elements of SAS Programming Style |
Frank C. Dilorio,
Jean Cocteau |
|
6 |
| SESUG |
1995 |
Using PROC EXPAND with Time Series Data |
Andrew H. Karp |
|
4 |
| SEUGI |
1995 |
SAS/SPECTRAVIEW: Building a Three Dimensional Periodic Table |
Dave Apter |
source |
8 |
| SUGI |
1995 |
A Comparison of the MODULE Routines with S-PLUS Extensibility |
Richard D. Langston |
source |
4 |
| SUGI |
1995 |
Adjusted F-Tests for Repeated Measures with the MIXED Procedure |
S. Paul Wright |
source |
6 |
| SUGI |
1995 |
Comparing the SAS GLM and MIXED Procedures for Repeated Measures |
Ming Chang,
Russell D. Wolfinger |
source |
11 |
| SUGI |
1995 |
Fuzzy Joins using SOL |
Jack N. Shoemaker |
source |
10 |
| SUGI |
1995 |
MACRO Facility Enhancements for Release 6.11 |
Jeffrey A. Polzin,
Susan M. O'Connor |
source |
10 |
| SUGI |
1995 |
SAS ARRAYS: A Basic Tutorial |
Bruce F. Gilsen |
source |
10 |
| SUGI |
1995 |
SAS Macros Useful in Imputing Missing Survey Data |
Barbara Lepidus Carlson,
Brenda Cox,
Linda Bandeh |
source |
6 |
| SUGI |
1995 |
Some Clever Things to Do with User Defined INFORMATS |
Ronald P. Cody |
source |
6 |
| SCSUG |
1994 |
Two Macros for Calculating Often Used Statistics of Symmetry and Agreement in the Biopharmaceutical Industry |
Jeff Heckathorn,
Mark F. Munsell |
|
12 |
| WUSS |
1994 |
Advantages and Pitfalls of the SQL Procedure |
Patricia L. Gerend |
source |
9 |
| WUSS |
1994 |
Checking Multiple Values for Inequality |
Stephen R. Schulz |
source |
2 |
| WUSS |
1994 |
Data Step Trigonometry for the Universal NO Symbol |
Kim J. Kubasek |
source |
2 |
| WUSS |
1994 |
Visualization of Scientific Data: Specialty Graphs Without Using Annotate |
Arthur L. Carpenter |
source |
9 |
| WUSS |
1994 |
Writing SAS Programs that Work |
Susan J. Slaughter,
Lora D. Delwiche |
source |
6 |
| NESUG |
1994 |
%SUBSTR and Other Useful Macro Functions |
Paula B. Podolnick |
|
9 |
| NESUG |
1994 |
Avoiding, Accepting and Taking Advantage of Missing Data |
Warren Repole Jr. |
|
10 |
| NESUG |
1994 |
DATA Step Efficiency and Performance |
Jeffrey A. Polzin |
|
7 |
| NESUG |
1994 |
Data Step Techniques: Reading, Writing, and Redacting |
August V. Treff |
|
10 |
| NESUG |
1994 |
Fuzzy Joins Using SQL |
Jack N. Shoemaker |
|
10 |
| NESUG |
1994 |
Maintenance Free Code Using the CONTENTS Procedure |
Denis Michel,
Elaine Michel |
|
5 |
| NESUG |
1994 |
New Tricks for an Old Tool: Using Custom Formats for Data Validation and Program Efficiency |
S. David Riba |
|
8 |
| NESUG |
1994 |
Some Clever Things to Do with User Defined INFORMATS |
Ronald P. Cody |
|
6 |
| MWSUG |
1994 |
DATA Step Efficiency and Performance |
Jeffrey A. Polzin |
|
7 |
| MWSUG |
1994 |
Letting SAS Find the Data Errors When You Have Multiple Observations and/or Multiple Datasets |
Deb Cassidy |
|
9 |
| MWSUG |
1994 |
New Tricks for an Old Tool: Using Custom Formats for Data Validation and Program Efficiency |
S. David Riba |
|
8 |
| SESUG |
1994 |
A Macro to Convert SAS Data Sets into Lotus 123 Files |
H. Ian Whitlock |
|
2 |
| SESUG |
1994 |
APPLICATIONS DEVELOPMENT An Implementation of SASe in an Environmental Information System |
Teresa James |
|
6 |
| SESUG |
1994 |
New Tricks for an Old Tool: Using Custom Formats for Data Validation and Program Efficiency |
S. David Riba |
|
8 |
| SESUG |
1994 |
SAS 6.09 on a CONVEX C3840: First Impressions |
Kenneth A. Hardy,
Sally M. Muller |
|
8 |
| SEUGI |
1994 |
Automatic Generation of Clinical Individual Data Documents, Using Connectively to Access Oracle Data on PC |
Gaetane Van der Borght |
source |
4 |
| SEUGI |
1994 |
SAS System-based Verification of Double Data Entry in a Focus Database |
Andre Gravel |
source |
22 |
| SUGI |
1994 |
An Automated Approach to Patient Exclusion Accountability: Summaries Using SAS Macro Language |
Carl Arneson,
Randall Austin,
Beth Sherrill,
Cam Huffman,
Greg Silva |
source |
5 |
| SUGI |
1994 |
An Introduction to PROC TABULATE: A Hands-on Workshop |
Christopher Toppe |
source |
6 |
| SUGI |
1994 |
Avoiding, Accepting and Taking Advantage of Missing Data |
Warren Repole Jr. |
source |
9 |
| SUGI |
1994 |
DATA Step Efficiency and Performance |
Jeffrey A. Polzin |
source |
7 |
| SUGI |
1994 |
Discovering the FUN in SAS Functions |
Neil H. Howard |
source |
6 |
| SUGI |
1994 |
Getting the Most from the SAS LOG |
Robert G. Hall,
Judith H. Mopsik |
source |
6 |
| SUGI |
1994 |
Report Production System-"Report Line " |
Samuel Berestizhevsky,
Tanya Kolosova |
source |
4 |
| SUGI |
1994 |
SAS 6.09 on a CONVEX C3840: First Impressions |
Kenneth A. Hardy,
Sally M. Muller |
source |
8 |
| SUGI |
1994 |
Some Clever Things to Do with User Defined INFORMATS |
Ronald P. Cody |
source |
6 |
| SUGI |
1994 |
Spreadsheet Data Gathering for the SAS System |
William C. Murphy,
Dennis H. Leverett |
source |
3 |
| SUGI |
1994 |
The SAS System under ConvexOS, Release 6.09 |
Isom Crawford,
Leigh A. Ihnen |
source |
4 |
| SUGI |
1994 |
The View Review: PROC SQL Views, SAS/ACCESS Views, and DATA Step Views |
Andrew A. Norton |
source |
6 |
| SUGI |
1994 |
Using the SAS System to Score the SF-36 Health Survey |
Katherine Newvine |
source |
6 |
| SUGI |
1994 |
Visualization of Scientific Data: Specialty Graphs Without Using Annotate |
Arthur L. Carpenter |
source |
9 |
| SUGI |
1994 |
You Want to Produce 83,000 Graphs Using SAS Software on a PC? You Gotta Be Kidding! |
Mark Bercov,
Ned Etris |
source |
6 |
| NESUG |
1993 |
A Routine to Convert Missing Numeric Values to Zero |
Paul H. Grant Jr. |
|
1 |
| NESUG |
1993 |
A Utility Program that Generates Descriptive Statistics on the Attributes of SAS Variables |
Larry Specht,
Charles K. Skee |
|
11 |
| NESUG |
1993 |
Beauty in Truth: Application of Logical Arithmetric |
August V. Treff |
|
7 |
| NESUG |
1993 |
Calculating Missing Value Counts |
Loren Lidsky |
|
2 |
| NESUG |
1993 |
Converting Data on the Macintosh into Permanent SAS Data Sets on the VAX System |
Jimmy Thomas Efird,
John Coen,
Phil Graceffa |
|
3 |
| NESUG |
1993 |
Survial Data Analysis - Life Table Methods |
Wei Zhang |
|
10 |
| SCSUG |
1993 |
Mini-Tips for Maxi-Programmers |
Jimmy A. DeFoor |
|
22 |
| MWSUG |
1993 |
Analysis of Incomplete Categorical Repeated Measures |
Charles S. Davis |
|
6 |
| MWSUG |
1993 |
PC Data Conversion: Avoiding the Pitfalls |
Daniel R. Bretheim |
|
6 |
| MWSUG |
1993 |
The View Review: PROC SQL Views, SAS/ACCESS Views, and DATA Step Views |
Andrew A. Norton |
|
6 |
| SEUGI |
1993 |
A New Look at the Facilities in PROC MIXED |
James L. Roger |
source |
12 |
| SEUGI |
1993 |
Extensions to Multivariate Descriptive Analysis |
Yves Roy |
source |
14 |
| SEUGI |
1993 |
The Analysis of Repeated Measures Designs Using the SAS System |
Alun Bedding |
source |
9 |
| SEUGI |
1993 |
The SAS System for OpenVMS Migrating from VAX to RISC Architecture |
Thomas J. R. Emmerich |
source |
5 |
| SUGI |
1993 |
A Model for Multivariate Repeated Measures with Missing Data |
Richard R. Allen |
source |
5 |
| SUGI |
1993 |
Beyond Column Input: Advanced Techniques with the INFILE and INPUT Statements |
Kathy McLeod,
Nancy Mae Bonney |
source |
10 |
| SUGI |
1993 |
Clinical Data Processing Efficiency Techniques |
Denis Michel |
source |
6 |
| SUGI |
1993 |
Data Compression and the SAS System |
Philip Bardsley |
source |
6 |
| SUGI |
1993 |
Date Manipulation Techniques, or How D'ya Show a Date a Good Time? |
Jerry Kagan |
source |
10 |
| SUGI |
1993 |
Discovering the FUN in SAS Functions |
Neil H. Howard |
source |
5 |
| SUGI |
1993 |
Filling in the Blanks: Multiple Imputation for Replacing Missing Values in Survey Data |
P. M. Wright |
source |
3 |
| SUGI |
1993 |
Frequency Tables Using the SAS System: What They Are and What They Could Be |
Grant Blank |
source |
4 |
| SUGI |
1993 |
PC Data Conversion: Avoiding the Pitfalls |
Daniel R. Bretheim |
source |
6 |
| SUGI |
1993 |
Planning, Preparing, Documenting, and Referencing SAS Products |
RoJeanne Liu,
Arthur Foreman |
source |
6 |
| SUGI |
1993 |
SAS Data Dictionary Tool Box |
Allan E. Williams,
Peter Dueber,
Lynette M. Browne |
source |
5 |
| SUGI |
1993 |
The SAS System for OpenVMS Migrating from VAX to RISC Architecture |
Ken D. Worsham |
source |
5 |
| SUGI |
1993 |
Using SAS/ACCESS Software with PC Files and Databases |
Forrest Boozer |
source |
8 |
| SESUG |
1993 |
Auto-Editing: A Data Management Tool for Research |
William S. Calvert |
|
6 |
| SESUG |
1993 |
Repeated Measures Using PROC MIXED Instead of PROC GLM |
James H. Roger,
Michael Kenward |
|
10 |
| SESUG |
1993 |
Space Saving Tools: Compression in SAS 6.07 |
Philip Bardsley |
|
9 |
| SCSUG |
1992 |
Discovering the FUN in Functions |
Neil H. Howard |
|
5 |
| SCSUG |
1992 |
The SAS System Supervisor - A Version 6 Update |
Merry G. Rabb,
Donald J. Henderson,
Jeffrey A. Polzin |
|
8 |
| NESUG |
1992 |
An In-Depth Look at the PHREG Procedures |
Peter P. Gaccione |
|
9 |
| NESUG |
1992 |
Creating Numberic Collating Sequences Using the NOBS Data Set Option |
Gary L. Katsanis |
|
2 |
| NESUG |
1992 |
Data/Variables Reduction by Principal Componets, Battery Reduction and Variable Clustering |
Joseph Massoro,
Zhini Zhang |
|
11 |
| NESUG |
1992 |
PROC SUMMARY: Enhancing Output with _TYPE_, Formats and Data Steps |
August V. Treff |
|
6 |
| NESUG |
1992 |
SAS System Supervison - A Version 6 Update |
Merry G. Rabb,
Donald J. Henderson,
Jeffrey Polzin |
|
8 |
| NESUG |
1992 |
Using SAS/FSP and SCL to Manage the Interaction Between End Users and the Application |
Felix G. Meale,
Nancy Mae Bonney |
|
3 |
| MWSUG |
1992 |
DATA Step Performance |
Pat M. Herbert,
Jeffrey A. Polzin |
|
5 |
| MWSUG |
1992 |
Management of Time-Range Data |
Andrew A. Norton |
|
6 |
| MWSUG |
1992 |
The Advantages of SAS 6.06 Under OS/2 |
Stephen Mandel |
|
4 |
| SEUGI |
1992 |
Data Step Performance and I/O Improvements in Release 6.07 |
Sarah Bell |
source |
9 |
| SEUGI |
1992 |
Database Features Extend the Scope of SAS/SHARE Software |
Colin J. Harris |
source |
6 |
| SEUGI |
1992 |
Writing a 4 GL for Statistical Analysis of Customised Databases |
Eoin O'Brien,
Neil Atkins |
source |
9 |
| SUGI |
1992 |
A SAS Macro for Performing Analyses of Random Effect Models of Longitudinal Data Using the IML Procedure |
Greg A. Grandits,
Patricia Grambsch,
Thomas Louis |
source |
6 |
| SUGI |
1992 |
A SAS Macro to Extend the Friedman Test for the Unbalanced Case Plus a Multiple Comparisons Procedure |
Kirk A. Easley |
source |
4 |
| SUGI |
1992 |
A SAS Program to Compute Factors for One-Sided Tolerance Limits for a Normal Distribution |
Susan Portugal |
source |
4 |
| SUGI |
1992 |
A Simulation Study of Estimators for Rates of Changes in Longitudinal Studies with Attrition |
Fong Wang,
Mary Lange |
source |
6 |
| SUGI |
1992 |
Analysis of Incomplete Categorical Repeated Measures |
Charles S. Davis |
source |
6 |
| SUGI |
1992 |
Creating a Data Dictionary for Large SAS Data Sets |
Steven A. Wilson |
source |
6 |
| SUGI |
1992 |
Data Exchange Between the SAS System and Microsoft Excel |
Louise Weiler,
Raymond Wan |
source |
6 |
| SUGI |
1992 |
DATA Step Performance |
Pat M. Herbert,
Jeffrey A. Polzin |
source |
4 |
| SUGI |
1992 |
Data-Checking Methods in a Data Entry System Based on SAS Software: Exploring the Possibilities |
Sandi Tennyson |
source |
5 |
| SUGI |
1992 |
Database Features Extend the Scope of SAS/SHARE Software |
William D. Clifford |
source |
6 |
| SUGI |
1992 |
How to Gain a Working Knowledge of the FREQ Procedure without Freaking Out |
Wendy London |
source |
8 |
| SUGI |
1992 |
Interactive Data Analysis Strategies |
Alvin M. Best III |
source |
6 |
| SUGI |
1992 |
Optimal Clinical Data Structures for Use with SAS/PH-Clinical Software |
Sandra D. Schlotzhauer,
Andrew T. Fagan |
source |
8 |
| SUGI |
1992 |
Simple SAS Analyses That Add Value to Corporate Databases |
Richard Sue |
source |
4 |
| SUGI |
1992 |
The SAS System Supervisor - A Version 6 Update |
Merry G. Rabb,
Donald J. Henderson,
Jeffrey A. Polzin |
source |
8 |
| SUGI |
1992 |
Using SAS/AF Software and SCL in Version 6 |
Karen Crandall |
source |
4 |
| SUGI |
1992 |
YTD Reporting Using the SUMMARY and TRANSPOSE Procedures |
Tom Bellmer,
Steven J. First |
source |
5 |
| SCSUG |
1991 |
Nearest Neighbor Hot-Deck Imputation for Missing Value With SAS/IML |
Thomas W. Sager,
James P. Gise,
M. W. Hemphill |
|
9 |
| NESUG |
1991 |
Fuzzy Merges (How to Make MERGE Really Work) |
Jack N. Shoemaker |
|
6 |
| NESUG |
1991 |
Is A Picture Really Worth a Thousand Words? |
James A. Sannerud |
|
5 |
| NESUG |
1991 |
Using SAS Procedures as Data Checking Tools |
Paula B. Podolnick |
|
5 |
| MWSUG |
1991 |
Post-processing of the SAS Log |
William E. Adams |
|
5 |
| MWSUG |
1991 |
SAS Efficiency Techniques |
Steven J. First,
Kim L. Kolbe |
|
10 |
| SEUGI |
1991 |
SAS/ACCESS Software to DB2 for Decision Support in a Large Industrial Organization (Conversion, Experiences, Requirements) |
P.J. M. van Kleef |
source |
6 |
| SEUGI |
1991 |
The Value of Missings |
Aad J. van Strien |
source |
6 |
| SUGI |
1991 |
Analysis of Sparse Repeated Categorical Measurement Data |
Alan Agresti,
Stuart Lipsitz,
Joseph B. Lang |
source |
9 |
| SUGI |
1991 |
Analyzing a Survey: A Bunch of Tips for a Mess of Questions |
William G. LeBlanc |
source |
4 |
| SUGI |
1991 |
Database Update with Incremental Audit Trail and Rollback Using the FSEDIT Procedure and Screen Control Language |
William R. Fehlner |
source |
4 |
| SUGI |
1991 |
ERROR: What to Look for When Debugging SAS Programs |
Brit Minor |
source |
6 |
| SUGI |
1991 |
Is a Picture Really Worth a Thousand Words? |
James A. Sannerud |
source |
5 |
| SUGI |
1991 |
Manipulating SAS Data Sets: An Introduction |
Richard Alonso |
source |
6 |
| SUGI |
1991 |
SAS Macros for Converting Between ARC/INFO Import/Export Files and SAS Data Sets |
Barbara L. Jackson |
source |
6 |
| SUGI |
1991 |
SAS Software Puzzles '91: The Debugging Continues |
Warren Repole Jr. |
source |
7 |
| SUGI |
1991 |
Using the C Language with SAS/TOOLKIT Software |
Oliver Bradley |
source |
11 |
| SUGI |
1991 |
Writing SAS/IML Functions to Call Subroutine Library Functions |
Richard D. Langston |
source |
6 |
| NESUG |
1990 |
Advanced Input Techniques |
Judith H. Mopsik,
Jeffrey Phillips |
|
8 |
| NESUG |
1990 |
Basics of the INPUT Statement |
Robert Virgile |
|
7 |
| NESUG |
1990 |
Handling Data Exclusions in a Clinical Trials DBMS |
Thomas R. Hoffman |
|
10 |
| NESUG |
1990 |
The SAS Supervisor - A Version 6 Update |
Donald J. Henderson,
Merry G. Rabb,
Jeffrey A. Polzin |
|
11 |
| NESUG |
1990 |
Understanding PROC UNIVARIATE Statistics |
Wendell F. Refior |
|
5 |
| NESUG |
1990 |
Working with Arrays |
Ronald P. Cody |
|
8 |
| MWSUG |
1990 |
The Painless Path to Release 6.06 of the SAS System |
Robina G. Thornton,
John C. Boling |
|
7 |
| MWSUG |
1990 |
The SAS Supervisor - A Version 6 Update |
Donald J. Henderson,
Merry G. Rabb,
Jeffrey A. Polzin |
|
10 |
| SEUGI |
1990 |
Using Relational Databases (ORACLE and RD) Together with the SAS System on VMS |
Thomas J. R. Emmerich |
source |
5 |
| SUGI |
1990 |
A Hierarchical Database Engine for Version 6 of the SAS' System |
Barbara A. Barrett |
source |
6 |
| SUGI |
1990 |
Aids in Data Checking |
William G. LeBlanc |
source |
5 |
| SUGI |
1990 |
ANALYSIS OF COVARIANCE: REPEATED IfEASURES AND SPLIT-Plur DESIGNS |
George A. Milliken |
source |
10 |
| SUGI |
1990 |
Analysis of Covariance: Repeated Measures and Split-Plot Designs |
Gilda P. Pareja |
source |
5 |
| SUGI |
1990 |
Calculating the Jonckheere-Terpstra Test Statistic Using the SAS System under VMS. . |
Steven Bailey |
source |
4 |
| SUGI |
1990 |
Developing User Applications Using the FSVIEW Procedure in Release 6.06 SAS/FSP Software |
Scott L. Bass |
source |
7 |
| SUGI |
1990 |
Overview of the SET, UPDATE, AND MERGE Statements |
Richard Alonso |
source |
5 |
| SUGI |
1990 |
Segmenting the Tourist Market Using Factor Analysis and Cluster Analysis |
Marcus Schmidt,
Hans Merser |
source |
6 |
| SUGI |
1990 |
The Impact of Machine Architecture on the Implementation of the SAS System |
Larry J. Noe |
source |
6 |
| SUGI |
1990 |
The Painless Path to Release 6.06 of the SAS System |
Robina G. Thornton,
John C. Boling |
source |
6 |
| SUGI |
1990 |
The SAS Supervisor - A Version 6 Update |
Donald J. Henderson,
Merry G. Rabb,
Jeffrey A. Polzin |
source |
9 |
| SUGI |
1990 |
Using Relational Database Technology and the SAS System to Develop SAS' System Applications under the VMS "Operating System |
Jean Moorefield |
source |
7 |
| SUGI |
1990 |
Using the SAS System on a Microvax for a Computer Assisted Interviewing System |
Veronica Fasullo,
Thomas Trumble |
source |
4 |
| SUGI |
1990 |
What SAS/IML Software Has to Offer beyond the MATRIX Procedure |
Donna E. Woodward |
source |
7 |
| SUGI |
1990 |
Working with Arrays |
Ronald P. Cody |
source |
7 |
| NESUG |
1989 |
Adjusting Time Series Data |
Jim Wimbish |
|
9 |
| NESUG |
1989 |
Converting Data Between "Foreign" Formats and SAS System Files |
Ronald P. Cody |
|
8 |
| SEUGI |
1989 |
(SAPS) Standard Analysis Program System |
Reiner Uhl |
source |
6 |
| SEUGI |
1989 |
Analysing Multiple Response Data Using PROC TABULATE |
Jan Bakker |
source |
6 |
| SEUGI |
1989 |
Data On Data |
Karsten Boye Rasmussen |
source |
11 |
| SEUGI |
1989 |
Using New SAS Database Features And Options |
Jack Wallace |
source |
12 |
| SUGI |
1989 |
A Generalized Editing Procedure for Questionnaire-based Data Using the SAS System |
Lance W. Cameron,
Lawrence R. Catlett |
source |
7 |
| SUGI |
1989 |
Analysis of a Repeated Measures Experiment with Missing Data |
Richard R. Allen |
source |
5 |
| SUGI |
1989 |
Analyzing Multiple Response Data Using the TABULATE Procedure |
Tina Keene |
source |
6 |
| SUGI |
1989 |
Most Creative Use of the Software-Monochrome |
Leslie Worf |
source |
116 |
| SUGI |
1989 |
Robust and Exploratory Analyses Automated |
E. James Harner,
Gerald R. Hobbs |
source |
5 |
| SUGI |
1989 |
SAS Tutorial Session: Reading and Writing Data to External Files |
Ronald P. Cody |
source |
|
| SUGI |
1989 |
SAS/ETS Software: A Report on the New. Features in Release 6.03 and Other Work in Progress |
Mark Little |
source |
4 |
| SUGI |
1989 |
Using New SAS Database Features And Options |
Stephen M. Beatrous,
William D. Clifford,
Kevin Mosmon,
John T. Stokes |
source |
12 |
| NESUG |
1988 |
The Right Approach to Learning PROC TABULATE |
Robert Virgile |
|
10 |
| SEUGI |
1988 |
A Theoretical and Operational Comparison Between SAS/IML and APL |
Antonio Giusti,
Giovanni Marchetti |
source |
19 |
| SEUGI |
1988 |
New Developments in Psychometric and Market Research Procedures |
Gerhard Held |
source |
5 |
| SEUGI |
1988 |
Version 6 Data Base System Architecture Current and Future Features |
Jack Wallace |
source |
9 |
| SUGI |
1988 |
Logit Models for Ordered Categorical Response Data |
Alan Agresti |
source |
9 |
| SUGI |
1988 |
New Developments in Pyschometric and Market Research Procedures |
Warren F. Kuhfeld,
Forrest W. Young |
source |
5 |
| SUGI |
1988 |
PROC DICHOT: A SAS Procedure for Creating Dichotomous Variables |
Paul Lambert |
source |
6 |
| SUGI |
1988 |
Version 6 SAS Data Base System Architecture: Current and Future Features |
Stephen M. Beatrous,
William D. Clifford |
source |
10 |
| SEUGI |
1987 |
Host/Mainframe Integration Using the SAS System |
Phil Lucas |
source |
12 |
| SEUGI |
1987 |
Numeric Precision Considerations When Using the SAS System |
Carlo Grandi |
source |
6 |
| SEUGI |
1987 |
The Wild Side of ANNOTATE Facility |
Bernie Ruane |
source |
16 |
| SUGI |
1987 |
"Beeperless "Programming-Effective Methods of Testing Using SAS Software |
Neil H. Howard,
James B. Pearson Jr.,
Linda Williams Pickle |
source |
8 |
| SUGI |
1987 |
A SAS Macro for Estimating Missing Values in Multivariate Data |
Gina M. Capalbo,
James S. Roberts |
source |
3 |
| SUGI |
1987 |
An Application That Interfaces PC and Host Resources |
Fritz Lehman |
source |
4 |
| SUGI |
1987 |
An Enhanced SAS/AF Software Consistency Error Checking Routine |
J. David Burks |
source |
3 |
| SUGI |
1987 |
Getting the Most from PROC TABULATE |
Andrew A. Norton |
source |
10 |
| SUGI |
1987 |
Supporting End-User Computing through Full-Featured SAS Files |
Robert Langill |
source |
6 |
| SUGI |
1987 |
The Wild Side of. the Annotate Facility |
Chip Kelly |
source |
8 |
| SUGI |
1987 |
Time Series Intervention Analysis Using SAS Software |
Terry J. Woodfield |
source |
9 |
| SUGI |
1987 |
Using SAS Software to Pass Data to IBM's Graphical Data Display Manager |
Rameshwar Ramotar |
source |
5 |
| SUGI |
1986 |
A Generalized SAS Algorithm Including Error Diagnostics for the Switchback Experimental Design with Three or More Treatments |
Nancy K. Keith,
George D. Williams |
source |
3 |
| SUGI |
1986 |
How to Write SAS Procedures for Version 5 |
Ronald Parker |
source |
10 |
| SUGI |
1986 |
If It Were a Snake, It Would Have Bit Ya, or 101 Things on the SASware Ballot You Can Already Do |
Neil H. Howard,
Merry G. Rabb |
source |
4 |
| SUGI |
1986 |
Inference from the Incomplete Longitudinal Design: A Collection of SAS Macros |
James Rochon |
source |
6 |
| SUGI |
1986 |
PERSONYRS: A SAS Procedure for Person Year Analyses |
Glenn A. Augustine,
Erik J. Bergstralh,
Jon L. Kosanke,
Kenneth P. Offord |
source |
6 |
| SUGI |
1986 |
Some SAS Utility Macros |
Akos Felsovalyi |
source |
6 |
| SUGI |
1986 |
What SAS Software Does and Doesn't Do: Things Your Mother Never Told You |
Calvin D. Croy |
source |
4 |
| SEUGI |
1985 |
New Features of the SAS/ETS and SAS/OR Software in Version 5 |
John C. Brocklebank |
source |
37 |
| SUGI |
1985 |
Changes and Enhancements to SAS/ETSSoftware |
David M. DeLong,
Leigh A. Ihnen,
Mark Little |
source |
12 |
| SUGI |
1985 |
Estimating Autocorrelations and Crosscorrelations where Missing Values are Present: A SAS Macro |
Pan-Yu Lai,
Kenneth M. Portier |
source |
5 |
| SUGI |
1985 |
Macros for Paired Categorical or Continuous Scale Data Analysis |
Thomas E. Bradstreet |
source |
8 |
| SUGI |
1985 |
More Efficient Data Storage when PROC TRANSPOSE is Used for Displaying the Output |
Lynn T. Julian,
Donald D.M. Tong |
source |
6 |
| SEUGI |
1984 |
A Procedure for Creating Table Look-Up Functions from SAS Data Sets |
A. D. Forbes |
source |
5 |
| SEUGI |
1984 |
New SAS/GRAPH Features |
Alan Russell |
source |
11 |
| SEUGI |
1984 |
New Statistical Features in SAS |
Gerhard Held |
source |
13 |
| SUGI |
1984 |
Changes and Enhancements to SAS/ETS Procedures |
David M. DeLong,
Leigh A. Ihnen,
Mark Little |
source |
6 |
| SUGI |
1984 |
Computing for Unbalanced Repeated Measures Experiments |
Kenneth N. Berk |
source |
7 |
| SUGI |
1984 |
New SAS Tools for Data Base Management |
N. D. Prabhakar |
source |
3 |
| SUGI |
1984 |
NODIS Procedures for Demographic Analysis |
Mark J. Salling,
Janice S. Tobin |
source |
6 |
| SUGI |
1984 |
PROC FSCALC in the Estimating Environment |
B. J. Collins,
Robert T. Grant |
source |
3 |
| SUGI |
1984 |
Progress and Prospects |
Richard A. Usanis |
source |
6 |
| SUGI |
1984 |
SAS Methods for the Rank Regression Model with Missing Values |
R. Michael Rubison,
Michael K. Thran |
source |
5 |
| SUGI |
1984 |
STRATI F: A Procedure for Producing Stratified Variables |
M. G. Sri Ram |
source |
5 |
| SUGI |
1984 |
Use of the SAS and SAS/FSP Products to Make the MSA/QPAC-Salary Package 'Online' |
Hans D. de Roos |
source |
2 |
| SEUGI |
1983 |
Archival Storage of SAS Files |
Karsten Boye Rasmussen |
|
8 |
| SUGI |
1983 |
A Financial Plotting System for Analysts and Managers |
Laura S. Vendeland |
source |
6 |
| SUGI |
1983 |
Analyzing Congressional Career Patterns Using SAS |
Robert G. Brookshire,
Claudia C. Putnam |
source |
7 |
| SUGI |
1983 |
Interactive Least Squares Estimation of Missing Values in any GLM |
Paul D. Hamilton,
Patric Dale McCray,
Michael C. Palmer |
source |
4 |
| SUGI |
1983 |
Living with the AHA: The Construction of a Multi-Year Hospital Information Data Base Using the AHA Annual Survey and SAS |
Mireille Leger,
Frederick E. Pratter |
source |
|
| SUGI |
1982 |
An Integration of System 2000 and Statistical Analysis System (SAS) |
Roger E. Booker |
source |
7 |
| SUGI |
1982 |
Approaches to Estimating Covariance Matrices with Partial Multinomial Data |
Mark W. Riggs,
W.B. Smith |
source |
10 |
| SUGI |
1982 |
Enhancements in ALSCAL-82 |
Forrest W. Young |
source |
10 |
| SUGI |
1982 |
How to Avoid Typing and Proofreading PROC FREQ Tables |
Vicki L. Cline,
Carlos M. Puig |
source |
6 |
| SUGI |
1982 |
Interim Error Handling Strategies in a SAS Based Research Data Management System |
David H. Christiansen,
James D. Hosking |
source |
6 |
| SUGI |
1982 |
SAS MACRO "REP2 "for Unweighted Means Analysis of Repeated Measures |
Robert V. Zupkis |
source |
8 |
| SUGI |
1982 |
SAS Procedures for ANOVA or Repeated Measures Designs |
William G. Jackson Jr.,
Richard C. McNee,
Carolyn J. Oakley |
source |
2 |
| SUGI |
1982 |
Set Merge and Update |
Peter L. Rikard |
source |
4 |
| SUGI |
1981 |
A Mid-Range Sensitivity Planning Model for Dyes and Chemicals |
Patricia A. Hermes,
James M. Watts |
source |
6 |
| SUGI |
1981 |
Alternative Geographical Display Strategies for Bivariate Relationships |
Michael P. Farrell,
Rodney H. Strand,
Joyce C. Waterhouse |
source |
5 |
| SUGI |
1981 |
Management Information Systems Using SAS as an Aid |
Stephen B. Sloan |
source |
|
| SUGI |
1981 |
Missing Data in Multivariate Linear Models: A Comparison of Several Estimation Techniques |
James D. Hosking |
source |
|
| SUGI |
1981 |
PROC BACKUP: Creating Raw Data Files from SAS Data Sets |
David H. Christiansen,
James D. Hosking |
source |
6 |
| SUGI |
1981 |
Some Real-Data Examples for Teaching Analysis for Experimental Designs |
Kaye H. Fendt,
Ronald W. Helms |
source |
7 |
| SUGI |
1981 |
Using the SIR Data Management System as a Complement to SAS |
Jonathan Arlook |
source |
|
| SUGI |
1981 |
Variance Estimates from Complex Survey Data: The Balanced Half-Sample Replication Procedure |
Josefina A. Lago |
source |
5 |
| SUGI |
1980 |
A Macro for Multivariate Randomization Analyses of Stratified Sample Data |
Ingrid A. Amara,
Gary G. Koch |
source |
11 |
| SUGI |
1980 |
Nonmetric Multidimensional Scaling Under SAS |
Robert M. Hamer,
Rostyslaw Lewyckyj,
Forrest W. Young |
source |
5 |
| SUGI |
1980 |
Recent Developments in BMDP of Special Interest to SAS Users |
James W. Frane |
source |
8 |
| SUGI |
1980 |
SAS as a Central Statistical System |
Peter Beutel |
source |
4 |
| SUGI |
1980 |
Shaded Map Reports |
Edward L. Spitznagel |
source |
|
| SUGI |
1979 |
A Data Dictionary Driven Data Analysis System |
Richard A. Enz,
Doron Steger |
source |
5 |
| SUGI |
1979 |
A Multivariate Approach to the Analysis of a Repeated Measures Experiment Using SAS |
Rocco L. Brunelle,
David W. Johnson |
source |
5 |
| SUGI |
1979 |
A Recode Procedure for SAS |
Douglas D. Cockrell,
Ione H. Cockrell |
source |
2 |
| SUGI |
1979 |
Application of Stepwise MANOVA and MANCOV Using SAS |
Chandu M. Patel |
source |
9 |
| SUGI |
1979 |
Retrieval of Value Labels from SPSS Files for SAS Use and the Creation and Maintenance of a SAS Format Library |
David H. Culver,
Richard D. Langston |
source |
2 |
| SUGI |
1977 |
Producing SAS Files from Larger Master Files for a Clinical Research Project |
Frank E. Harrell Jr. |
source |
3 |
| SUGI |
1977 |
SAS as Programming Tool and Central Data Interface with Other Statistical Software |
Douglas Cockrell,
Mike Foxworth,
Gary Griepentrog |
source |
|
| SUGI.ONE |
1976 |
A Comparison of BMD, SAS and SPSS |
T. Papaioannou,
G.P.H. Styan,
L.L. Ward |
source |
21 |
| SUGI.ONE |
1976 |
Data Management for Environmental Research |
Rodney H. Strand |
source |
18 |
HOME
QUICK SEARCH
AUTHORS
KEYWORDS
HELP
BUGS