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308 papers and 1 presentation; 279 titles.

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Conference × Year × Title × Authors Extras Pages
WUSS 2026 Demystifying Regression Analysis Techniques with R Zheyuan Yu,
Kirk Paul Lafler,
Zichun Gao,
Jiaxin Xu,
Zeqi Li,
Ruochen Shao		 35
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 Transitioning from SAS to R: Implementing Reproducible R Workflows for TLF Validation Poornima Alavandi source 17
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
WUSS 2025 Automating Clinical Study Reports: A Modular Framework for Continuous Data Reporting William Wei source 7
PharmaSUG 2025 Leveraging Python for Statistical Analysis in Public Health: Techniques and Visualizations for Life Sciences Professionals Michael Carnival source 14
PHUSE EU 2024 A programmer's introduction to modelling with proc mixed, focusing on how to debug and validate, common mistakes and how to interpret the output George Gibson source
presentation source		 6
WUSS 2024 Regression Analysis Made Easy Using SAS Studio Zheyuan (Walter) Yu source 42
PharmaSUG 2024 Semi-Automated and Modularized Approach to Generate Tables for Clinical Study - Categorical Data Report William Wei,
Shunbing Zhao		 source 8
PharmaSUG 2024 Visual discovery in Risk-Based Monitoring using topological models Kostiantyn Drach,
Iryna Kotenko		 source 31
PHUSE EU 2023 Increasing Trend of Biomarker Analysis in Clinical Trials Anjali Ranga source
presentation source		 11
WUSS 2023 Leveraging Logistic Regression and Bootstrap Sampling to Assess a Decision Threshold in Classification Predictive Analytics Colleen E. McGahan source 15
WUSS 2023 Open-Source Essentials HOW - R and RStudio Wendy Christensen source 17
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
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
PharmaSUG 2023 Smart Use of SAS Output System and SAS Macro for Statistic Test Selection Mengxi Wang source 10
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
SESUG 2022 Using SAS Macro and ODS Output to efficiently examine the descriptive and analytic statistics in epidemiology studies Yue Pan source 7
SESUG 2021 Back to Basics: Running an Analysis from Data to Refinement in SAS Deanna Naomi Schreiber-Gregory source 21
SESUG 2021 Coarsening Continuous Variables: An Automated Method to Create Categorical Version of Continuous Variables While Mitigating Loss of Precision Zach Acuff source 10
SESUG 2021 Moving beyond Frequency and Percentage to Chi Square, t-Tests, and Correlation Analysis Kelly D. Smith source 12
SESUG 2021 Using the SAS HPBIN Procedure to Create Format Value Ranges for Numeric Variables Michael A. Raithel source 9
PharmaSUG 2021 Back to Basics: Running an Analysis from Data to Refinement in SAS Deanna Naomi Schreiber-Gregory source 21
PharmaSUG 2021 Data Visualization in Real World Data (RWD) and Health Economics Outcomes Research (HEOR) using (Statistical Analysis Software) SAS Swapna Ambati source 45
SGF 2021 Back to Basics: Running an Analysis from Data to Refinement in SAS Deanna Naomi Schreiber-Gregory source
presentation		 20
SGF 2021 Investing in Education Intelligently Sam Edison,
Rushya Puttam,
Akhil Emani,
Nithisha Katta		 source
presentation		 7
SGF 2021 Reporting Correlation Coefficient Results and Plots: A SAS Macro That Does It All Zhengming Chen,
Bowen Zheng		 source
presentation		 11
SGF 2020 An Efficient Way to Create Descriptive Tables with Pairwise Comparison Ang Gao,
Chul W. Ahn		 source 10
SGF 2020 Automation in SAS Visual Data Mining and Machine Learning Wendy Czika source 26
SGF 2020 Moving from Messy Data to a Clean Analytic Dataset: Common Data Management Techniques using SAS Bethany A. Bell source 27
SGF 2020 Use SAS to Model Non-Linearity Xiaoting Wu,
Donald S. Likosky		 source 12
PharmaSUG 2020 A Brief Introduction to Performing Statistical Analysis in SAS, R & Python Erica Goodrich,
Daniel Sturgeon		 source 22
PharmaSUG 2020 Special Plots methods with diabetes disease data Yida Bao,
Zheran Wang,
Jingping Guo,
Philippe Gaillard		 source 10
PharmaSUG 2020 You Are Using PROC GLM Too Much (and What You Should Be Using Instead) Peter L. Flom,
Deanna Naomi Schreiber-Gregory		 source 15
SESUG 2019 A user-friendly and robust macro that produces a publication-ready Table 1 Daniel Brinton,
Marshall Chew		 source 5
SESUG 2019 Data-driven Programming Techniques using SAS Macros to Semi-automate Generation of Descriptive Tables in Healthcare Research Katie Mercaldi source 37
SESUG 2019 Macros for Neural Network Modeling Ross Bettinger source 23
SESUG 2019 Medians/quartiles are shown for continuous data and counts/percentages are shown for categorical data Martha Wetzel source 5
SESUG 2019 The Thorn in My Side!! Logistic Regression Continuous Variables that Violate the Assumption of Linearity on the Log-odds (Logit) Scale: How to Identify and What to Do? Janet M. Grubber,
Cynthia J. Coffman		 source 13
MWSUG 2019 Classifying Risk in Life Insurance using Predictive Analytics Sai Gopi Krishna Govindarajula 6
MWSUG 2019 Comparison of three methods for transforming predictor variables to improve model fit using SAS Douglas Thompson 13
MWSUG 2019 Survival Tips for Survival Analysis Xiaoting Wu 13
WUSS 2019 Choosing Wisely: Using the Appropriate Statistical Test for Trend in SAS Christina Park,
Jui-Ting Hsiung,
Melissa Soohoo,
Elani Streja		 source 10
PharmaSUG 2019 Create publication-ready variable summary table using SAS macro Geliang Gan source 12
PharmaSUG 2019 One Macro to Produce Descriptive Statistic Summary Tables with P-Values Rajaram Venkatesan,
Harinarayanan Gopichandran		 source 14
PharmaSUG 2019 The Power of Data Visualization in R Oleksandr Babych source 21
PHUSE US 2019 A SAS Macro for Variable Ratio Treatment vs. Placebo in Propensity Score Matching Punjita Baranwal,
Prashant Kulkarni		 source
presentation source		 13
PHUSE EU 2018 Collinearity in Mixed Models John Hendrickx source
presentation source		 11
SESUG 2018 Table Taming - Using ODS to Simplify Data Presentation Emily Perry source 12
SESUG 2018 Using SAS to create HTML codebooks and more! Ethan Ritchie source 9
MWSUG 2018 Quality Control for Big Data: How to Utilize High Performance Binning Techniques Deanna Naomi Schreiber-Gregory,
Karlen Bader		 12
WUSS 2018 Validating User-Submitted Data Files with Base SAS Michael A. Raithel source 16
PharmaSUG 2018 Using ODS LAYOUT, GTL, and ODSTEXT to Generate a Compact Graphical Codebook Shane Rosanbalm source 4
SGF 2018 Data Quality Control for Big Data: Preventing Information Loss with High-Performance Binning Deanna Naomi Schreiber-Gregory source 9
SGF 2018 Estimation of Correlation Coefficient in Data with Repeated Measures Katherine Irimata,
Paul Wakim,
Xiaobai Li		 source 19
SGF 2018 Getting Started with Survival Analysis Marc Huber source 76
SGF 2018 New Ways to Incorporate Continuous Predictors in Generalized Linear Models Anthony Salis,
Angela Wu,
Gordon Johnston		 source 15
SGF 2018 Using Information Value, Information Gain, and Gain Ratio for Detecting Two-Way Interaction Effect Alec Zhixiao Lin source 19
SGF 2018 Validating User-Submitted Data Files with Base SAS Michael A. Raithel source 16
SESUG 2017 Data Quality Control: Using High Performance Binning to Prevent Information Loss Deanna Naomi Schreiber-Gregory,
Henry M. Jackson		 source 9
SCSUG 2017 Predictive Analysis for Classifying Type 2 Diabetes Patients Using SAS Enterprise Miner 14.1 and SAS Enterprise Guide 7.1 Saurabh Sanjayrao Kokad source 10
PHUSE EU 2017 The TFL Workbench:From Pilot to Production Iain Humphreys,
Hansjörg Frenzel		 source
presentation source		 11
MWSUG 2017 Data Quality Control: Using High Performance Binning to Prevent Information Loss Lakshmi Nirmala Bavirisetty,
Deanna Naomi Schreiber-Gregory		 9
WUSS 2017 A Method for Simultaneously Programming Mock and Computed Tables Michael Williams source 8
WUSS 2017 Integrating Health Data Sources to Identify, Stratify, and Predict High Utilizers of Public Systems Benjamin Knisley,
Joshua Morgan,
Linn Carothers,
Keith Haigh		 source 15
WUSS 2017 Standardized Approaches for Analyzing Quality Improvement Interventions in Healthcare Settings Polina Kukhareva source 20
PharmaSUG 2017 Data Quality Control: Using High Performance Binning to Prevent Information Loss Deanna Naomi Schreiber-Gregory,
Lakshmi Nirmala Bavirisetty,
Kaushal Chaudhary		 source 9
PharmaSUG 2017 Dealing with alignment of statistics: Beyond the decimal point. Eric Pedraza source 11
PharmaSUG 2017 Multiple Imputation: A Statistical Programming Story Christopher J. Smith,
Scott Kosten		 source 16
PharmaSUG 2017 Survival 101 - Just Learning to Survive Leanne Goldstein,
Rebecca Ottesen		 source 20
SGF 2017 A SAS Macro for Covariate Specification in Linear, Logistic, and Survival Regression Sai Liu source
zip		 19
SGF 2017 An Easy-to-Use SAS Macro for a Descriptive Statistics Table Yuanchao Zheng,
Jin Long,
Maria E. Montez-Rath		 source
pdf		 16
SGF 2017 Continuous Predictors in Regression Analyses Ruth Croxford source 12
SGF 2017 Diving Deep into ODS Graphics Styles Dan Heath source 19
SGF 2017 Getting Classy: A SAS Macro for CLASS Statement Automation Erica Goodrich,
Daniel Sturgeon,
Kathryn Schurr		 source
pdf		 12
SGF 2017 The ODS EXCEL statement: Tips and Tricks for the TABULATE and REPORT Procedures Richard A. DeVenezia source 17
SGF 2017 The Three Amigos Factors: Determining Term Deposit Purchases: How a Bank Can Get Other People's Money Gina Colaianni,
Bogdan Gadidov,
Matthew Mitchell		 source 59
SESUG 2016 A Simple SAS Macro to Perform Blinder-Oaxaca Decomposition Taylor Lewis,
Stanislas Ezoua		 source 8
SESUG 2016 Enhanced Swimmer Plots: Tell More Sophisticated Graphic Stories in Oncology Studies Ilya Krivelevich,
Andrea Dobrindt,
Simon Lin,
Xiaomin He		 source 9
SESUG 2016 Using SAS to Examine the relationship between primary caregivers' Adverse Childhood Experiences (ACE) and Child Abuse Allegations Abbas S. Tavakoli,
Kate K. Chappell,
Senna Dejardins		 source 9
PHUSE EU 2016 Considerations in the Use of Propensity Scoresin Observational Studies Lawrence Rasouliyan,
Estel Plana		 source
presentation source		 17
WUSS 2016 Introduction to Survival Analysis in SAS Leanne Goldstein,
Rebecca Ottesen		 source 19
PharmaSUG 2016 Handling Interim and Incomplete Data in a Clinical Trials Setting Paul Stutzman source 13
PharmaSUG 2016 What makes a "Statistical Programmer" different from a "Programmer" Arun Raj Vidhyadharan,
Sunil Mohan Jairath		 source 12
SGF 2016 Applying Frequentist and Bayesian Logistic Regression to MOOC data in SAS: a Case Study Yan Zhang,
Ryan Baker,
Yoav Bergner		 source
presentation		 7
SGF 2016 Improving Performance of Memory-Based Reasoning Model by Using Weight of Evidence Coded Categorical Variables Vinoth Kumar Raja,
Goutam Chakraborty,
Vignesh Dhanabal		 source 6
SGF 2016 Restricted Cubic Spline Regression: A Brief Introduction Ruth Croxford source 5
MWSUG 2015 A Macro for Systematic Treatment of Special Values in Weight of Evidence Variable Transformation Chaoxian Cai 9
MWSUG 2015 Cross-Cultural Comparison of the School Factors Affecting Students' Achievement in Mathematical Literacy: Based on the Multilevel Analysis of PISA 2012 Yage Guo 6
MWSUG 2015 Cutpoint Determination Methods in Survival Analysis Using SAS: Updated %FINDCUT Macro Jeffrey P. Meyers,
Jayawant N. Mandrekar		 7
WUSS 2015 Chi-Square and T-Tests Using SAS: Performance and Interpretation Jennifer L. Waller,
Maribeth H. Johnson		 source 12
WUSS 2015 ODS Output Data Sets That Work for You Stuart D. Long,
Edward Heaton,
Daniel R. Scharf		 source 29
PharmaSUG 2015 Statistical Analyses Across Overlapping Time Intervals Based on Person-Years John Reilly,
John R. Gerlach		 source 10
SGF 2015 A Case Study: Improve Classification of Rare Events with SAS Enterprise Miner Ruizhe Wang,
Novik Lee,
Yun Wei		 source
zip		 12
SGF 2015 Chi-Square and T-Tests Using SAS: Performance and Interpretation Jennifer L. Waller,
Maribeth H. Johnson		 source 12
SGF 2015 Cutpoint Determination Methods in Survival Analysis Using SAS: Updated %FINDCUT Macro Jayawant N. Mandrekar,
Jeffrey P. Meyers		 source 8
SGF 2015 Data Sampling Improvement by Developing the SMOTE Technique in SAS Lina Maria Guzman Cartagena source 9
SGF 2015 Don't Copy and Paste--Use BY Statement Processing with ODS to Make Your Summary Tables Jeffrey M. Gossett,
Mallikarjuna Rettiganti		 source 18
SGF 2015 Getting Your Hands on Reproducible Graphs Rebecca Ottesen,
Leanne Goldstein		 source 19
SGF 2015 Improving the Performance of Data Mining Models with Data Preparation Using SAS Enterprise Miner Ricardo Galante source 21
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 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 Statistical Analysis of Publicly Released Survey Data with SAS/STAT Software SURVEY Procedures Donna Brogan source 20
SGF 2015 Using and Understanding the LSMEANS and LSMESTIMATE Statements David J. Pasta source 20
SGF 2015 Using SAS Enterprise Miner to Predict Breast Cancer at an Early Stage Gibson Ikoro,
Beatriz de la Iglesia		 source 9
SESUG 2014 %DISCIT Macro: Pre-screening continuous variables for subsequent binary logistic regression analysis through visualization Mohamed S. Anany source 6
SESUG 2014 A Strip Plot Gets Jittered into a Beeswarm Shane Rosanbalm source 12
SESUG 2014 Overview of Analysis of Covariance (ANCOVA) Using GLM in SAS Abbas S. Tavakoli source 10
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 Putting on the Ritz: New Ways to Style Your ODS Graphics to the Max Dan Heath 15
WUSS 2014 Using and Understanding LSMEANS and LSMESTIMATE David J. Pasta source 20
PharmaSUG 2014 Linking Healthcare Claims and Electronic Health Records (EHR) for Patient Management - Diabetes Case Study Paul A. LaBrec source 17
PharmaSUG 2014 Survival 101 - Just Learning to Survive Leanne Goldstein,
Rebecca Ottesen		 source 19
PharmaSUG 2014 Survival Analysis Approaches and New Developments using SAS Jane Lu,
David Shen		 source 13
SGF 2014 Analyzing Data from Experiments in Which the Treatment Groups Have Different Hierarchical Structures Sharon Lohr,
Peter Schochet		 source 12
SGF 2014 Association of Socio-Emotional Factors: Religious Affiliation, Depression to Suicidal Tendency Among Adolescent Girls Sesha Sai Ega,
Chandra Shekar Pulipati,
Venkata Rachapudi		 source 6
SGF 2014 CMS Core Measures, the Affordable Care Act, and SAS Visual Analytics Joe Whitehurst,
Diane Hatcher		 source 16
SGF 2014 Graphs Useful for Variable Selection in Predictive Modeling Bob Moore source 9
SGF 2014 Putting on the Ritz: New Ways to Style Your ODS Graphics to the Max Dan Heath source 15
SGF 2014 Time Contour Plots David J. Corliss source 9
SGF 2014 Using SAS Enterprise Miner to Predict the Injury Risk Involved in Car Accidents Prateek Khare,
Vandana Reddy,
Goutam Chakraborty		 source 7
WUSS 2013 A Quick and Dirty Descriptive Data Summary Nora Ruel,
Rebecca Nelson		 source 12
WUSS 2013 A Tutorial on PROC LOGISTIC Arthur X. Li source 19
WUSS 2013 Automated Forward and Best Subset Selection for Generalized Additive Models using PROC GAM William Garner source 11
WUSS 2013 Customize Comparison Tables for Clinical Studies Polina Kukhareva,
Katherine J. Roggenkamp		 source 12
WUSS 2013 Survival 101 - Just Learning to Survive Leanne Goldstein,
Rebecca Ottesen		 source 24
WUSS 2013 Tools for Imputing Missing Data Taylor Lewis source 16
SESUG 2013 Chi-Square and T-Tests Using SAS: Performance and Interpretation Jennifer L. Waller,
Maribeth H. Johnson		 source 12
SESUG 2013 Comparing SAS PROC MI and IVEware Callable Software Bruno Vizcarra,
Amang Sukasih		 source 11
MWSUG 2013 A Tutorial on PROC LOGISTIC Arthur X. Li 19
MWSUG 2013 Analyzing Continuous Variables from Complex Survey Data Using PROC SURVEYMEANS Taylor Lewis 14
MWSUG 2013 Improved Interaction Interpretation: Application of the EFFECTPLOT statement and other useful features in PROC LOGISTIC Robert G. Downer 8
MWSUG 2013 Introduction to REDCap for Clinical Data Collection Shannon M. Morrison 17
MWSUG 2013 Time Contour Plots David J. Corliss 11
NESUG 2013 Analyzing Continuous Variables from Complex Survey Data Using PROC SURVEYMEANS Taylor Lewis 15
NESUG 2013 The Smarts of Using Dummies Patricia Meholick 4
NESUG 2013 The Ultimate Macro to Generate Summary Tables Lan Hu,
Xiaoqiang Wang		 12
NESUG 2013 Time Contour Plots David J. Corliss 11
PharmaSUG 2013 Combining Analysis Results from Multiply Imputed Categorical Data Bohdana Ratitch,
Ilya Lipkovich,
Michael O'Kelly		 source 19
SGF 2013 Being Continuously Discrete (or Discretely Continuous): Understanding Models with Continuous and Discrete Predictors and Testing Associated Hypotheses David J. Pasta source 19
SGF 2013 Chi-Square and T-Tests Using SAS: Performance and Interpretation Jennifer L. Waller,
Maribeth H. Johnson		 source 12
SGF 2013 Cox Proportional Hazard Model Evaluation in One Shot Polina Kukhareva source 11
SGF 2013 DICHOTOMIZED_D: A SAS Macro for Computing Effect Sizes for Artificially Dichotomized Variables Aarti P. Bellara,
Harold Holmes,
Isaac Y. Li,
Jeanine L. Romano,
Jeffrey D. Kromrey,
Patrice Rasmussen,
Patricia Rodríguez de Gil,
Yi-Hsin Chen		 source 12
SCSUG 2012 Dirty Data? Clean it up with SAS. Gabriela Cantu source 11
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 How to Perform and Interpret Chi-Square and T-Tests Jennifer L. Waller source 14
SESUG 2012 ODS Output Data Sets That Work for You Stuart D. Long,
Edward Heaton,
Daniel R. Scharf		 source 29
MWSUG 2012 Swimming with Sharks: Using Formats with Summary Data Tom Bugg 8
WUSS 2012 Being Continuously Discrete (or Discretely Continuous): Understanding Models with Continuous and Discrete Predictors and Testing Associated Hypotheses David J. Pasta source 19
WUSS 2012 SAS Rule-Based Codebook Generation for Exploratory Data Analysis Ross Bettinger source 8
SGF 2012 Building WOE Binned LGD Scorecards using SAS Enterprise Miner Anthony J. van Berkel,
Naeem Siddiqi		 source 14
SGF 2012 Enhanced Data Analysis Using SAS ODS Graphics and Statistical Graphics Patricia A. Berglund source 20
SGF 2012 How to Perform and Interpret Chi-Square and T-Tests Jennifer L. Waller source 20
SGF 2012 SAS Tools for Cost-Effective and High-Quality Clinical Trial Reporting Paul J. Novotny,
Angelina Tan,
Nathan R. Foster,
Jeff A. Sloan		 source 10
SESUG 2011 Acknowledging the Unknown: A SAS Macro for Investigating Omitted Variable Bias in Two-Level Linear Models Jason A. Schoeneberger,
Bethany A. Bell,
Jeffrey D. Kromrey		 source 16
SESUG 2011 Tailoring Logistic Regression Model Analyses with the ODDSRATIO Statement in PROC LOGISTIC Taylor Lewis source 8
WUSS 2011 ChiD, A Chi-2 - Based Discretization Algorithm Ross Bettinger source 9
MWSUG 2011 Contour Plots for Time Series Analysis David J. Corliss 11
MWSUG 2011 Increased Life Expectancy from Positive Perceptions of Retirement Reuben Ng,
Deepak C. Lakra,
Becca R. Levy		 10
MWSUG 2011 Summary Statistics in Rows John Henry King 15
MWSUG 2011 Your Age In People Years: Not All Formulas Are the Same Arthur L. Carpenter 5
NESUG 2011 Glorified PROC CONTENTS for Secondary Source Data Bruce Thomas 15
NESUG 2011 SAS Does the Dirty Work, But How Do I Make It Look Pretty? Sarah Bobiak 12
PharmaSUG 2011 Macro to Generate Summary and Descriptive Statistics Tables Yogesh Pande source 6
PharmaSUG 2011 Your Age In People Years: Not All Formulas Are the Same Arthur L. Carpenter source 5
SGF 2011 BASE SAS Implementation of Information Theoretic Feature Selection for Neural Networks Martin B. Jetton source 13
SGF 2011 ODS Output Data Sets That Work for You Stuart D. Long,
Edward Heaton,
Daniel R. Scharf		 source 29
SGF 2011 Ratemaking Using SAS Enterprise Miner: An Application Study Billie Anderson source 20
SGF 2011 The Anti-Curse: Creating an Extension to SAS Enterprise Miner Using PROC ARBORETUM Andrew S. Cathie source 11
SGF 2011 Those Confounded Interactions: Building and Interpreting a Model with Many Potential Confounders and Interactions David J. Pasta source 13
SGF 2011 To FREQ, Perchance to MEANS Christopher J. Bost source 10
SGF 2011 Using Hash Tables to Obtain Matched Post-Hoc Control Populations Jeffrey Reiss,
Elayne Reiss		 source 7
SGF 2011 Writing SAS Programs with SAS and Microsoft Excel: Generate Batch SAS Programs Based on SAP Specifications Laiju Zhang source 7
NESUG 2010 A SAS Application to Identify and Evaluate Outliers Richard Hendra,
Paulette W. Staum		 16
NESUG 2010 To FREQ, Perchance to MEANS Christopher J. Bost 11
WUSS 2010 An Introduction to Statistical Tests for the SAS Programmer Sara Beck source 10
WUSS 2010 Statistical Analysis - The First Steps Jennifer L. Waller source 20
WUSS 2010 Those Confounded Interactions: Building and Interpreting a Model with Many Potential Confounders and Interactions David J. Pasta source 9
WUSS 2010 Where's The Match? Matching Cases and Controls after Data Collection Jennifer L. Waller source 6
WUSS 2010 Your Age In People Years: Not All Formulas Are the Same Arthur L. Carpenter source 4
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 Exploring, Analyzing, and Summarizing Your Data: Choosing and Using the Right SAS Tool from a Rich Portfolio Douglas Thompson 25
SESUG 2010 Developing a Model for Person Estimation in Puerto Rico for the 2010 Census Coverage Measurement Program Colt S. Viehdorfer source 12
SESUG 2010 PROC_CODEBOOK, Automating the Review and Documentation of SAS Files James Terry source 3
SESUG 2010 Statistical Analysis - The First Steps Jennifer L. Waller source 20
PharmaSUG 2010 An Overview of the CLASS, CONTRAST, and HAZARDRATIO Statements in the SAS 9.2 PHREG Procedure Paul T. Savarese,
Michael J. Patetta		 23
PharmaSUG 2010 The Ubiquitous Clinical Trials Data Summary Table "Summary Statistics in Rows" John Henry King 15
SGF 2010 An Introduction to Multiple Imputation of Complex Sample Data Using SAS 9.2 Patricia A. Berglund source 12
SGF 2010 An Overview of the CLASS, CONTRAST, and HAZARDRATIO Statements in the SAS 9.2 PHREG Procedure Paul T. Savarese,
Michael J. Patetta		 source 23
SGF 2010 Improving Your Statistical Consulting Prowess by Adding R to Your SAS Repertoire Mark Austen,
Jeffrey M. Gossett,
Maria S. Melguizo,
Chan-Hee Jo		 source 11
SGF 2010 Let SAS Do the Work: Correlation Crossroads Theresa Gilligan,
Cheryl Coon,
Lauren Nelson,
Lori D. McLeod,
Yung-Jui Yang		 source 18
SGF 2010 Using the SAS System and SAS Enterprise Miner for Data Mining: A Study of Cancer Survival at the Mayo Clinic Leonard Gordon source 6
MWSUG 2009 Ranking Predictors in Logistic Regression Douglas Thompson 13
PNWSUG 2009 Building Multiple Linear Regression Models - Food for Thought Colleen E. McGahan,
Mei Chong		 11
WUSS 2009 Automating the Process of Scoring a Generalized Linear Model Fitted using PROC GENMOD Prakash Gurumurthy source 9
PharmaSUG 2009 %ANYTL: A Versatile Table/Listing Macro Yang Chen 11
SGF 2009 Fitting Cox Model Using PROC PHREG and Beyond in SAS Lea Liu,
Sandy A. Forman,
Bruce A. Barton		 source 10
SGF 2009 Learning When To Be Discrete: Continuous vs. Categorical Predictors David J. Pasta source 10
SGF 2009 Test Linearity between Continuous Confounder and Binary Outcome First, Run Multivariate Regression Analysis Second Jiming Fang,
Peter C. Austin,
Jack V. Tu		 source 8
WUSS 2008 Learning When To Be Discrete: Continuous vs. Categorical Predictors David J. Pasta source 6
SESUG 2008 Interpreting Three-way Interactions Using SAS Anh Le,
Maribeth H. Johnson		 source 12
SESUG 2008 Utilizing Hash Tables to Obtain Matched Post-Hoc Control Populations Elayne Reiss,
Jeffrey Reiss		 source 9
PharmaSUG 2008 %YAMGAST: Yet Another Macro to Generate a Summary Table Zang Xiong,
St. Jude Children's		 10
PharmaSUG 2008 Automated SAS Macro for Adverse Event Logistic Regression Analysis Suhas R. Sanjee 8
PharmaSUG 2008 The Logic and Logistics of Logistic Regression Lawrence Rasouliyan,
Dave P. Miller		 14
SGF 2008 Adventures in ODS: Producing Customized Reports Using Output from Multiple SAS Procedures Stuart D. Long,
Jeffrey M. Abolafia,
Lawrence Park		 source 23
SGF 2008 Using SAS/OR to Improve Service Completion at a Fortune 100 Company Chuck Kelly,
Jinxin Yi,
Philip Easterling		 source 9
NESUG 2007 Using SAS ODS to Extract and Merge Statistics from Multiple SAS Procedures into a Single Summary Report, a Detailed Methodology Stuart D. Long,
Jeffrey M. Abolafia,
Lawrence Park		 22
SESUG 2007 Application of an Exact Covariate Matching Macro in Outcome Analysis Guiping Yang source 19
MWSUG 2007 Identifying and Overcoming Common Data Mining Mistakes Doug Wielenga 20
WUSS 2007 Recreating the SELECTION=SCORE Model Specification with the BEST=n Effect Selection Option for PROC SURVEYLOGISTIC Sara R. Adams,
David R. Mink,
Dave P. Miller		 source 8
PNWSUG 2007 Missing Data? A Look at Two Imputation Methods Anita Rocha 8
PharmaSUG 2007 A Review of Propensity Score Application in Healthcare Outcome and Epidemiology Guiping Yang 24
PharmaSUG 2007 A SAS Macro for Rapid Checking the Data Records Heng Zou 8
SGF 2007 Attitudes toward Immigrants and Immigration in the United States: Toward a Structural Approach Michael J. Sobczak source 12
SGF 2007 Effect Modification Investigation Using SAS - A Model-Building Exercise Vanessa Bundy,
Paule Barbeau,
Maribeth H. Johnson		 source 11
SGF 2007 Identifying and Overcoming Common Data Mining Mistakes Doug Wielenga source 20
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
PHUSE EU 2006 Save Time: Program Your Output Specs Hansjörg Frenzel,
James Witt		 source 11
SESUG 2006 Creating RTF Tables with Univariate Analyses of Multiply Imputed Data Marie Gantz source 14
SESUG 2006 Effect Modification Investigation Using SAS - A Model Building Exercise Vanessa Bundy,
Paule Barbeau,
Maribeth H. Johnson		 source 10
SESUG 2006 ST09 Application of the Generalized Linear Model to the Prediction of Lung Cancer Survival Andrew Karem source 18
SESUG 2006 Using SAS ODS to Extract and Merge Statistics from Multiple SAS Procedures into a Single Summary Report, a Detailed Methodology Stuart D. Long source 21
WUSS 2006 Clean Up Your Act: Data Cleaning with SAS Mel Widawski source 9
WUSS 2006 The Logic and Logistics of Logistic Regression Lawrence Rasouliyan,
Dave P. Miller		 source 14
NESUG 2006 How To Produce Almost Perfect RTF Output Suzanne M. Dorinski 20
PharmaSUG 2006 %SummaryTable: A SAS Macro to Produce a Summary Table in Clinical Trial Yinmei Zhou,
St. Jude Children's		 7
SUGI 2006 Displaying the Similarity of a Case and Matched Controls Robert S. Wilson source 8
SUGI 2006 The "Handy, Dandy, Quick-n-Dirty" Contrast Generator - A SAS/IML Macro to Support the GLM, MIXED, and GENMOD Procedures Paul A. Thompson source 10
SUGI 2006 Using SAS ODS to Extract and Merge Statistics from Multiple SAS Procedures Stuart D. Long,
Jeffrey M. Abolafia,
Lawrence Park		 source 21
SESUG 2005 Using ODS to Perform Simulations on Statistics from SAS Procedures Andrew A. Kramer source 4
MWSUG 2005 Information Discovery Using Decision Trees Morgan C. Wang 79
NESUG 2005 %DIAGNOS: a Macro for a Quick Look Into the Inside of Your Data Set Yin You 3
NESUG 2005 Fast and Easy Ways to Check Your Work: Using PROC MEANS to Confirm Continuous Variable Categorizations Richard M. Mitchell 2
PharmaSUG 2005 Calculating Posterior Probability of the Maximum Contrast Using PROC IML Akira Wakana,
Isao Yoshimura,
Chikuma Hamada		 6
SUGI 2005 An Easy-to-Use SAS Table Formatting Macro: Stand-Alone, Flexible, and Quick Zbigniew A. Kadziola source 8
SUGI 2005 Parameterizing Models to Test the Hypotheses You Want: Coding Indicator Variables and Modified Continuous Variables David J. Pasta source 20
SESUG 2004 Where's The Match? Jennifer L. Waller,
Verna C. Brantley,
Robert H. Podolsky		 source 6
WUSS 2004 Parameterizing Models to Test the Hypotheses You Want: Coding Indicator Variables and Modified Continuous Variables David J. Pasta source 12
WUSS 2004 Using Classification Tree Outcomes to Enhance Logistic Regression Models Tim Millard source 4
MWSUG 2004 Evaluating Continuous Variable Transformations in Logistic Regression Maggie (Yu) Zhang,
Shaolin Chen,
Suzanne C. Rain		 12
MWSUG 2004 Presenting the Binary Logit Model Effectively using SAS Hun Myoung Park 11
PharmaSUG 2004 A Macro Generating a Report-Ready Table in Different Layout and Format Renda Cheng 12
PharmaSUG 2004 Traditional Repeated Measures Analysis Versus Random Coefficients Models Using PROC MIXED Radhi Abdulnabi 9
SUGI 2004 Assessing Model Fit and Finding a Fit Model Margaret L. Bogle,
Rajiv Goel,
Robert M. Hamer,
Chan-Hee Jo,
Eric Siegel,
Pippa M. Simpson		 source 17
WUSS 2003 JMP-code Corner Charles E. Shipp,
Kirk Paul Lafler		 source 2
SEUGI 2003 Text Mining for tourism - The world of typical high quality restaurants in Piedmont [presentation] Roberto Fontana source 78
PharmaSUG 2003 Capabilities and Example Uses of a SAS Macro for Summary Reporting of Clinical Data James T. Decker,
Jun Wang		 8
SUGI 2003 Continuous or Not: How One Can Tell Vatsala V. Karwe source 2
SUGI 2003 Cutpoint Determination Methods in Survival Analysis Using SAS Jayawant N. Mandrekar,
Sumithra J. Mandrekar,
Stephen S. Cha		 source 5
SUGI 2003 Fast and Easy Ways to Annoy a SAS Programmer: A Statistician's Revenge! Richard M. Mitchell source 10
SUGI 2003 P-Value Generation Simplified with a Single SAS Macro Christopher S. Hord,
Peter J. Anderson		 source 3
MWSUG 2002 JMP Density Ellipse Script Code Made Easy Charles E. Shipp,
Kirk Paul Lafler		 2
NESUG 2002 Estimating Confidence Intervals Around Relative Changes in Outcomes in Segmented Regression Analyses of Time Series Data Fang Zhang,
Anita Wagner,
Stephen B. Soumerai,
Dennis Ross-Degnan		 7
SESUG 2002 JMP Density Ellipse Script Code Made Easy Charles E. Shipp,
Kirk Paul Lafler		 source 2
WUSS 2002 Doing Big Demographic Segmentation with the Combination of Multivariate Procedures and Macro Facilities Charles Sun source 6
WUSS 2002 JMP Density Ellipse Script Code Made Easy Charles E. Shipp,
Kirk Paul Lafler		 source 6
WUSS 2002 Multiple Linear Regression Analysis of Total Mercury Levels in Blood and Urine Samples Miki Yamauchi,
George C. J. Fernandez,
Stanley Omaye		 source 6
SUGI 2002 Fast and Easy Ways to Annoy a SAS Programmer: A Statistician's Revenge! Richard M. Mitchell source 10
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 Fast and Easy Ways to Check Your Work: Using PROC MEANS to Confirm Continuous Variable Categorizations Richard M. Mitchell source 2
MWSUG 2001 Modeling Lifetime Value in the Insurance Industry C. Olivia Parr Rud 9
NESUG 2001 Analyzing Complex Sample Survey Data: A New Beginning Linda Tompkins,
Arlene B. Siller		 3
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
WUSS 2001 Factor Analysis of Categorical Data in SAS Lei Han,
Torsten B. Neilands,
Margaret Docini		 source 6
WUSS 2001 Is Your Data Clean? Three Steps in Data Cleaning Mel Widawski source 5
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 Format Challenges with PROC REPORT Lisa M. Schneider source 6
SUGI 2001 Modeling Lifetime Value in the Insurance Industry C. Olivia Parr Rud source 8
SESUG 2000 Data Mining: An Overview of Methods and Technologies for Increasing Profits in Direct Marketing C. Olivia Parr Rud source 7
NESUG 2000 NPV Modeling for Direct Mail Insurance C. Olivia Parr Rud 7
NESUG 2000 Saving Paper and Making it Pretty: A SAS Macro for Simplifying and Enhancing Preliminary Analysis of Continuous Data Benjamin E. Leiby 7
WUSS 2000 Data Warehousing for Data Mining: A Case Study C. Olivia Parr Rud source 9
PharmaSUG 2000 Using SAS Macro to List the Frequencies and Statistics in a Summary Table Kinwah Fung,
Toronto Sunnybrook		 6
SUGI 2000 Data Warehousing for Data Mining: A Case Study C. Olivia Parr Rud source 9
SESUG 1999 Data Mining: An Overview of Methods and Technologies for Increasing Profits in Direct Marketing C. Olivia Parr Rud source 7
WUSS 1999 Data Mining: An Overview of Methods and Technologies for Increasing Profits in Direct Marketing C. Olivia Parr Rud source 6
NESUG 1999 Data Mining: An Overview of Methods and Technologies for Increasing Profits in Direct Marketing C. Olivia Parr Rud 7
SUGI 1999 Data Mining: An Overview of Methods and Techniques for Increasing Profits in Direct Marketing C. Olivia Parr Rud source 7
SUGI 1999 The Science and Art of Market Segmentation Using PROC FASTCLUS Mark E. Thompson source 5
SUGI 1999 Using SAS Macro to Include Statistics Output in Clinical Trial Summary Table Amy C. Young,
Sharon X. Zhou		 source 5
SUGI 1999 Using SAS/INSIGHT Software as an Exploratory Data Mining Platform Robin Way source 6
NESUG 1998 Data Mining: An Overview of Methods and Techniques for Increasing Profits in Direct Marketing C. Olivia Parr Rud 8
SESUG 1998 Data Mining: An Overview of Methods and Technologies for Increasing Profits in Direct Marketing C. Olivia Parr Rud 7
SESUG 1998 Programmatic Generation of Table Specifications Using the SAS Macro Facility Weimin "WilHam" Sun,
Caroline Stewart		 10
PNWSUG 1998 The Science and Art of Market Segmentation Using PROC FASTCLUS Mark E. Thompson 6
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
NESUG 1997 Rankings and Multi-Criteria Evaluation of Alternatives: Usage of Objective Data, Subjective Measurements and Expert Judgement Pavel Brusilovskiy 7
SEUGI 1997 A genetic algorithm for improving a priori variables partition for risk segmentation in third party liability insurance Alberto Busetto,
Andrea Costa		 source 8
SEUGI 1997 The Use of the Data Table to Develop New Variables for Customer Selection Martin Beasley,
Richard Cannon		 source 13
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 Generic Methodology in Report Building by Using SAS Macros Irene Zhao source 5
NESUG 1996 Macros Developed for Statistical Summary and Tabulation of Clinical Data William Sun,
David Lago,
David Handelsman,
Jack Shostak		 10
NESUG 1996 Poor Mans's Missing Value Recovery Methods Leonardo Auslender 10
SUGI 1996 New Age Marketing: Past Life Regression vs. LogisticRegression C. Olivia Parr Rud source 5
MWSUG 1995 Matching Observations on Both Continuous and Categorical Variables: A Simple Application of the FASTCLUS Procedure Manuel S. Lombardero 6
SESUG 1995 New Age Marketing: Past Life Regression versus Logistic Regression C. Olivia Parr Rud 9
SUGI 1995 Matching Observations on Both Continuous and Categorical Variables: A Simple Application of the FASTCLUS Procedure Manuel Lombardero source 6
SUGI 1993 SAS Data Dictionary Tool Box Allan E. Williams,
Peter Dueber,
Lynette M. Browne		 source 5
SUGI 1993 SAS Software to Fit the Generalized Linear Model Gordon Johnston source 8
SUGI 1992 Using the SAS System for Medical Data Processing and Event Analysis: An Overview Harald Pitz,
Marcus Frenz,
Hans-Peter Howaldt		 source 6
NESUG 1991 Using SAS Procedures as Data Checking Tools Paula B. Podolnick 5
MWSUG 1991 FALSEPOS: A Macro for Sensitivity, Specificity, Postive and Negative Predictive Values and Receiver Operating Characteristic (ROC) Curves Alfred J. Anderson 6
SUGI 1990 Interactive Random Sampling Ashok Mehta,
Paul H. Bern		 source 3
SUGI 1990 Resampling-Based Multiple Testing Peter H. Westfall,
Youling Lin,
S. Stanley Young		 source 6
SUGI 1988 PROC DICHOT: A SAS Procedure for Creating Dichotomous Variables Paul Lambert source 6
SUGI 1983 An Economical Approach to Verifying the Accuracy of Keypunch Data Steven N. Blair,
Nancy N. Goodyear		 source 6
SUGI.ONE 1976 General Linear Model Procedure James H. Goodnight source 39

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