HOME QUICK SEARCH AUTHORS KEYWORDS HELP BUGS

imputation

432 papers and 22 presentations; 430 titles.

Back to keywords

Conference × Year × Title × Authors Extras Pages
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
PharmaSUG CN 2026 A Standardized Programming Framework for Daily Dose Dataset Construction from Multi-Source Clinical Data Xianjun Li,
Chengye Lu		 source 10
PharmaSUG CN 2026 ADICE: A Proposed ADaM Dataset Structure for Intercurrent Events in the Estimand Framework Lijie Zhu source 11
PharmaSUG CN 2026 Efficient Strategies for Handling Data Issues in Clinical Trial Submissions Kate Sun source 7
PharmaSUG CN 2026 Evaluating Missing Data Imputation Methods for Clinical Trial Analysis Jiajun Wen source 12
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 A systematic approach for imputing missing dose information in population pharmacokinetic analysis datasets Prema Sukumar,
Renuka Hegde,
Erin Dombrowsky,
Neelima Thanneer		 source 7
PharmaSUG 2026 Case Study: Integrating ADPPK CDISC Standards into Pharmacometric Programming and Analysis Workflows Praseeda Rajan,
Renuka Hegde,
Erin Dombrowsky,
Neelima Thanneer,
Yue Zhao		 source 6
PharmaSUG 2026 Do One Task, Get Another Done for Free: Use DEFX Tags in Comments to Fill Out Your define.xml Brendan Bartley,
Megan Hinger		 source 21
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 Harmonizing History: A Framework for Deriving Line of Therapy in Complex Integrated Summaries. Bhargav Koduru,
Kavitha Guddam,
Santosh Reddy Lekkala		 source 15
PharmaSUG 2026 Human Beings Still Needed: Manual ADaM Checks that AI Can-t Do Sandra Minjoe,
Mario Widel		 source 9
PharmaSUG 2026 Improving AI SAS-to-R Code Migration via an Intermediate Design Document Layer Junze Zhang,
Amy Zhang		 source 7
PharmaSUG 2026 ISO 8601 and SAS-and R! A Practical Approach Derek P. Morgan,
Marckenley Mercie		 source 17
PharmaSUG 2026 Picking Up the Pieces: Implementation of (the forgotten) ADaM Naming Fragments Richann J. Watson,
Karl Miller		 source 7
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 Tipping Point Analysis: An Illustration of Sensitivity Analysis on Non-Administrative Censoring for Progression-Free Survival (PFS) Lihui Deng,
Kylie Fan,
Xiaoting Qin		 source 7
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 Bridging Analysis and Strategy: The Transformative Role of Statistical Analysts in Drug Development Lingsong Yun source
presentation source		 17
PHUSE US 2026 Building High-Quality SDTM from Real-World Data: Lessons and Solutions Peishan(Jenny) Han,
Lixiao Chen		 source
presentation source		 4
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 End-to-End Workflow for CDISC Estimand Framework in Clinical Trials Hiral Raval,
Simon Luo		 source
poster source		 12
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 Is Complexity Worth It? A Case Study Comparing Multiple Imputation Strategies Elnaz Ghadimi,
David Bushnell		 source
poster source		 4
PHUSE US 2026 Prompting for Precision: Leveraging Agentic Workflows to Accelerate Clinical Trial Document Understanding Jaya Simha Inampudi source
presentation source		 14
PHUSE US 2026 The Journey Beyond Anonymisation: Exploring the Secondary Use of Clinical Trial Data Yuliia Cherednichenko,
Tetiana Skorokhod		 source
presentation source		 18
PHUSE US 2026 Time to Roclet! admiral's use of custom roclets to enhance and extend examples in function documentation Benjamin Straub source
presentation source		 9
PHUSE APAC 2026 Accelerating iteration in R with Parallel Processing Sarita Singh source
presentation source		 9
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 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 SAS to R: Opportunities and Challenges in SDTM and ADaM Transformation Jalendhar Ettedi,
Padmashree Jahagirdar		 source
presentation source		 5
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 EU 2025 Bridging the Gap: Translating Intercurrent Events into Analysis-Ready ADaM Datasets Meghna Diedrich source
poster source		 9
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 From Hassle to Harmony: Unifying Code Quality Experience in R and SAS Michael Thomsen source
presentation source		 9
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 The Chicken, the Egg and the ADaM: Cracking the PRE-ADSL vs ADSL Sequence Carla Santillán,
Dubravka Georgijevic		 source
presentation 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 Mind the Gaps: Automating Multiple Imputation in Clinical Trial Workflows [presentation] Crisa Chen,
Jiangang Cai		 source 30
MWSUG 2025 Worried about that Second Date with ISO? Using PROC FCMP to Convert and Impute ISO 8601 Dates to Numeric Dates Richann J. Watson source 19
SESUG 2025 Comparing analysis method implementations in software (CAMIS) Brian K. Varney 4
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 Worried about that Second Date with ISO? Using PROC FCMP to Convert and Impute ISO 8601 Dates to Numeric Dates Richann J. Watson 19
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 Leveraging SAS DATA Step to Evaluate Government Programs by Repurposing Administrative Data: Strategies for Narrowing Data Scope, Imputing Missing Data, Mitigating Bias, and Streamlining Reporting Humberto Cisneros,
Richard Rivera		 source 16
WUSS 2025 Worried about that Second Date with ISO? Using PROC FCMP to Convert and Impute ISO 8601 Dates to Numeric Dates Richann J. Watson source 19
PharmaSUG 2025 Approaches to Developing Multiple Imputation ADaM Datasets Kang Xie source 8
PharmaSUG 2025 Checking Outside the Box: A Framework for Submission Success Julie Ann Hood,
Seiko Yamazaki		 source 13
PharmaSUG 2025 Efficient Strategies for Handling Data Issues in Clinical Trial Submissions Kate Sun source 7
PharmaSUG 2025 Get Started with ChatGPT and DeepSeek in SAS James Austrow source 17
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 Worried about that Second Date with ISO? Using PROC FCMP to Convert and Impute ISO 8601 Dates to Numeric Dates Richann J. Watson source 19
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 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
PHUSE EU 2024 Artificial Intelligence Potential Use Cases in Clinical Data Engineering Deepak Pakalapati,
Mounika Pillamarapu		 source
presentation source		 8
PHUSE EU 2024 Challenges Faced When Creating Dosing Records for a Population PK Dataset Marianne van Vugt,
Gerly van der Vleuten		 source
presentation source		 3
PHUSE EU 2024 Estimands: How to Make ADaM Implementation Your Friend Jaclyn Amberg 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 Unveiling the Tipping Point: An In-depth Analysis Varsha Mithun Patil,
Ephicacy Lifescience		 source
presentation source		 8
PHUSE EU 2024 Why Are Old Wines Sometimes Tastier Than New Ones? Lars Andersson,
Henning Pontoppidan Föh		 source
presentation source		 10
SESUG 2024 Comparative Tools in SAS: Analyzing Self-Reported versus Imputed Values Neesha Nathwani,
Sarah Woodruff		 source 30
WUSS 2024 Handling Missing Values in SAS 9 and SAS Viya [presentation] Melodie Rush source 102
WUSS 2024 Multiple Imputation in Interim Analyses [presentation] Bill Coar source 29
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 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 Oncology ADaM Datasets Creation Using R Programming: A Comprehensive Approach Ishwar Chouhan source 29
PharmaSUG 2024 Programming Considerations in Deriving Progression-Free Survival on Next-Line Therapy (PFS2) Alec McConnell,
Yun Peng		 source 5
PHUSE US 2024 An End User's Perspective of Clinical Dosing Data Used for Population PK/PD Analysis with Non-Linear Mixed Effects Modelling Rebecca Humphrey,
Mitali Gaurav		 source
poster source		 2
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 Missing to Meaningful: Tackling Partial Dates in Clinical Trials Bhargav Koduru source
poster source		 4
PHUSE US 2024 It is Time to Get Answers: Handling Questionnaire Data in Respiratory Studies Valeriia Oreshko source
presentation source		 10
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 Automating ADaM Dataset Generation via Definitive Specification Writing with A Domain-Specific Language Kang Zhao 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 The Hassle of Validating Global SAS Programs: Made Easy(ier) Michael Thomsen source
presentation source		 12
WUSS 2023 How Do We Git There? Best Practices for using Git with SAS Joe Matise source 8
SESUG 2023 CAMIS: Comparing Analysis Method Implementations in Software Brian K. Varney source 4
PharmaSUG 2023 Admiralonco - The Cross-Company R Package for Oncology Admirers Neharika Sharma,
Matthew Marino		 source 25
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 Sensitivity Analysis for Missing Data Using Control-based Pattern Imputation Jun Feng,
Jingmin Liu		 source 7
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
PHUSE US 2023 Computerised Algorithm for Multiple Myeloma Response Assessments Mahesh Swaminathan source
presentation source		 3
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 Up-to-Date with Dates - Imputation Rules in R Compared to SAS Monika Ludwinska source
presentation source		 10
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 Performance Traps in Multiple Imputation Analysis Using Mixed Model Repeated Measures [presentation] Piotr Karasiewicz source
video		 17
WUSS 2022 Child Data Set: An Alternative Approach for Analysis of Occurrence and Occurrence of Special Interest Lindsey Xie,
Richann J. Watson,
Jinlin Wang,
Lauren Xiao		 source 15
WUSS 2022 Finding Your Latest Date Bill Coar source 6
PharmaSUG 2022 ADaM Child Data Set: An Alternative Approach for Analysis of Occurrence and Occurrence of Special Interest Lindsey Xie,
Richann J. Watson,
Jinlin Wang,
Lauren Xiao		 source 15
PharmaSUG 2022 ADCM Macro Design and Implementation Chengxin Li,
Tingting Tian,
Toshio Kimura		 source 7
PharmaSUG 2022 Evaluating and Correcting Nominal and Actual Relative Time Measurements in Clinical Trials PK data Timothy J. Harrington source 17
PharmaSUG 2022 ISO 8601 and SAS: A Practical Approach Derek P. Morgan source 13
PharmaSUG 2022 Unravel the mystery around NONMEM data sets for statistical programmers Sabarinath Sundaram,
Lyma Faroz		 source 9
PHUSE US 2022 Design ADSL and ADBASE in Cell Therapy [presentation] Ryan Krause,
Xiang Wang		 source
video		 24
PHUSE US 2022 Pan-pharma Industry Collaboration - New Horizon, Success Stories [presentation] Tadeusz Lewandowski source
video		 21
PHUSE US 2022 Reassure Your Assumptions Using Sensitivity Analysis [presentation] Vadym Kalinichenko source
video		 29
PHUSE US 2022 Safety ADaM Automation from Design to Implementation [presentation] Chengxin Li,
Toshio Kimura		 source
video		 18
PHUSE EU 2021 The Application of Pattern Mixture Models and Tipping Point Analysis in Clinical Trials Involving Multiple Imputations [poster] Anastasiia-Narmina Fataliieva,
Daria Shamaida		 source
video		 1
SESUG 2021 Missing Value Imputation Ross Bettinger source 34
PHUSE US 2021 Using Real-world Data in Supporting Health Authority Submission [presentation] Ruoying Fang,
Shuang Zhang		 source
video		 26
PHUSE US 2021 Using the Computational Power of Julia to Drive Clinical Trial Simulations (CTS) [presentation] Patrick Hannon source 13
PharmaSUG 2021 Common Dating in R: With an example of partial date imputation Teckla Akinyi source 14
PharmaSUG 2021 Imputation for Missing Dosing Time in NONMEM PopPK Datasets Shuqi Zhao source 8
PharmaSUG 2021 The Tortoise and the Hare - Lessons Learned in Developing ADaM Training Alyssa M. Wittle source 6
PharmaSUG 2021 Using PROC FCMP to Create Custom Functions in SAS Keith Shusterman,
Mario Widel		 source 12
SGF 2021 A SAS Macro to Perform Kaplan-Meier Multiple Imputation for Survival Analyses with Competing Events Jialin Han,
Sai Liu,
Margaret R. Stedman,
Maria E. Montez-Rath		 source 8
PHUSE EU 2020 Data Standards for Non-interventional Studies [poster] Karen Horton,
Vladlen Ivanushkin		 source
video		 1
PHUSE EU 2020 Improve Programming and Traceability with Datasets for Statistical Analysis Results Henning Pontoppidan Föh,
Jeppe Juul		 source
video
presentation source		 4
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
SGF 2020 Analyzing Non-normal Data: Application to Missing Data Problems Niloofar Ramezani source 10
SGF 2020 Automation in SAS Visual Data Mining and Machine Learning Wendy Czika source 26
SGF 2020 Dealing with Missing Data in Epidemiological and Clinical Research Andrew T. Kuligowski,
Lida Gharibvand		 source 6
SGF 2020 How to Explain Your Black-Box Models in SAS Viya Funda Güneş source 26
PharmaSUG 2020 SDTM to ADaM Programming: Take the Leap! Jennifer McGrogan,
Alyssa M. Wittle		 source 5
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 Handling Missing Data in Clinical Trials: Techniques and Methods Pennidhi Karlakunta,
Naveen Kommuru		 source
presentation source		 9
PHUSE US 2020 Population PK Dataset: Basic Concepts and Challenges from the Perspective of a SAS Programmer Namrata Pokhrel source
presentation source		 12
PHUSE US 2020 The JumpStart Traceability Assessment Tejas Patel,
Sarah Ferkov		 source 10
PHUSE EU 2019 ADaM Programming Made Easy - Common ADaM Templates and an SAS Macro Library Niels-Kristian Kjøller source
poster source		 4
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 Challenges Involved with Using Legacy Clinical Trial Study Data During Integration Adebola Opasanya source
poster source		 8
PHUSE EU 2019 Customised AE Narratives Using JMP Clinical Alastair Scarlett,
Carol Vaughn		 source
presentation source		 4
PHUSE EU 2019 Early experiences using SDTM to organize clinical data collected using a physical activity tracker Martin Gram,
Gianluca Mortari		 source
presentation source		 13
PHUSE EU 2019 Multiple imputation as a valid way of dealing with missing data Vadym Kalinichenko source
presentation source		 11
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
WUSS 2019 LOAN DEFAULT PREDICTION Amarjeet Cheema,
Miriam McGaugh		 source 8
PharmaSUG 2019 ADME Study PK SDTM/ADaM And Graph Fan Lin source 9
PharmaSUG 2019 An Innovative Efficacy Table Programming to Automate Its Figure Generation to Ensure Both High Quality and Efficiency Xiangchen (Bob) Cui,
Letan (Cleo) Lin		 source 23
PharmaSUG 2019 Best Practices for ISS/ISE Dataset Development Bharath Donthi,
Lingjiao Qi		 source 9
PharmaSUG 2019 Experiences in Building CDISC Compliant ADaM Dataset to Support Multiple Imputation Analysis for Clinical Trials Xiangchen (Bob) Cui source 20
PharmaSUG 2019 Leveraging Intermediate Data Sets to Achieve ADaM Traceability Yun (Julie) Zhuo source 12
PharmaSUG 2019 More Traceability: Clarity in ADaM Metadata and Beyond Wayne Zhong,
Richann J. Watson,
Daphne E. Ewing,
Jasmine Zhang		 source 26
PharmaSUG 2019 Practices in CDISC End-to-End Streamlined Data Processing Chengxin Li source 14
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 Ushering SAS Emergency Medicine into the 21st Century: Toward Exception Handling Objectives, Actions, Outcomes, and Comms Troy Martin Hughes source 24
SGF 2019 Machine Learning and Predictive Analytics in SAS Enterprise Miner and SAS/STAT Software David Richard Cutler source 21
SGF 2019 Multiple Imputation for Skewed Multivariate Data: A Marriage of the MI and COPULA Procedures Zhixin Lun,
Ravindra Khattree		 source 9
SGF 2019 SAS Enterprise Miner and SAS Visual Data Mining and Machine Learning Hand Shake Jagruti Kanjia,
Dominique Latour,
Holly Sweeney		 source 35
SGF 2019 SCORE! Techniques for Scoring Predictive Regression Models Using SAS/STAT Software Phil Gibbs,
Randy Tobias		 source 18
PHUSE US 2019 Creating the Virologic Response ADaM Dataset Manjusha Alapati source
presentation source		 7
PHUSE US 2019 Dashboarding Analytics for Oncology Endpoint Data Using SAS Vidya Muthukumar source
presentation source		 7
PHUSE US 2019 Hit and Miss: An Evaluation of Imputation Techniques from Machine Learning Karma Tarap source
presentation source		 7
PHUSE US 2019 Implementing Imputation Rules and Creating Analysis Flags Following CDISC Guidelines Venkata Madhira,
Harish Yeluguri		 source
presentation source		 8
SCSUG 2018 Machine Learning in Litigation Prediction - Workers' Compensation Litigation Propensity Predictive Model Mei Najim source 17
PHUSE EU 2018 Can You Cut It? Implementing the Data Cut-off Lewis Meares source
presentation source		 9
PHUSE EU 2018 First Aid Kit for an Observational Study Yuliia Bahatska,
Vladlen Ivanushkin		 source
poster source		 4
PHUSE EU 2018 Handling Missing Data in Clinical Trials Using Topological Data Analysis Sergey Glushakov,
Andrey Rekalo		 source
presentation source		 10
PHUSE EU 2018 How to Write ADaM Specifications Like a Ninja Caroline Francis source
poster source		 5
PHUSE EU 2018 Optimizing the Traceability Method in ADaM Parallel Conversions Roxane Debrus source
presentation source		 15
MWSUG 2018 Avoiding Sinkholes: Common Mistakes During ADaM Data Set Implementation Richann J. Watson 15
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 Inevitable Use of RETAIN Statement in Multiple Scenarios with Examples for Creating Efficacy Parameters Prabhakara Rao Burma,
Venkata N Madhira		 source 5
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 The First and Last Words of ADaM Susan J. Kenny source 4
PharmaSUG 2018 ADINTDT and ADTTE for Survival Sweep in Oncology Studies Christine S Teng,
Wenyu Hu		 source 6
PharmaSUG 2018 Avoiding Sinkholes: Common Mistakes During ADaM Data Set Implementation Richann J. Watson,
Karl Miller		 source 18
PharmaSUG 2018 Challenges & Strategies in PKPD Programming Jing Su,
Jiannan Kang		 source 6
PharmaSUG 2018 Data Standards for Population Pharmacokinetic (POPPK) and Non-Compartmental Analysis (NCA) Neelima Thanneer source 7
PharmaSUG 2018 PK Data Explained Timothy J. Harrington source 9
PharmaSUG 2018 Speed Up SDTM/ADaM Dataset Development with If-Less Programming Lei Zhang,
John Lu		 source 17
SGF 2018 A Macro for Last Observation Carried Forward Jonson Jiang source 5
SGF 2018 Claim Analytics: A Litigation Prediction Case Study Mei Najim source 18
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 Using SAS for Multiple Imputation and Analysis of Longitudinal Data Patricia A. Berglund source 24
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
PHUSE EU 2017 ADaM Mapping - KeyConsiderations forMetadata Driven Realization Elenea Gathe source 12
MWSUG 2017 ADQRS: Basic Principles for Building Questionnaire, Rating and Scale Analysis Datasets Nancy Brucken,
Karin LaPann		 6
MWSUG 2017 Claim Analytics Mei Najim 18
MWSUG 2017 ISO 8601 and SAS: A Practical Approach Derek P. Morgan 9
MWSUG 2017 Multiple Imputation of Family Income Data in the 2015 Behavioral Risk Factor Surveillance System Jia Li,
Aaron Sussell		 4
MWSUG 2017 Using SAS to Compare Two Estimation Methods on the Same Outcome: Example from First Trimester Pregnancy Weights Brandy R. Sinco,
Edith Kieffer,
Kathleen Welch,
Diana Welmerink Bolton		 15
WUSS 2017 Avoiding Sinkholes: Common Mistakes During ADaM Data Set Implementation Richann J. Watson,
Karl Miller		 source 15
PharmaSUG CN 2017 How to define Treatment Emergent Adverse Event (TEAE) in crossover clinical trials Mengya Yin,
Wen Tan		 6
PharmaSUG 2017 ADQRS: Basic Principles for Building Questionnaire, Rating and Scale Analysis Datasets Nancy Brucken,
Karin LaPann		 source 6
PharmaSUG 2017 An Efficient Solution to Efficacy ADaM Design and Implementation Chengxin Li,
Zhongwei Zhou		 source 7
PharmaSUG 2017 Analyzing Singly Imputed Utility Based Weighted Scores Using EQ-5D for Determining Patients' Quality of Life in Oncology Studies Vamsi Krishna Medarametla,
Liz Thomas,
Gokul Vasist		 source 9
PharmaSUG 2017 Combining Survival Analysis Results after Multiple Imputation of Censored Event Times Jonathan L Moscovici,
Bohdana Ratitch		 source 11
PharmaSUG 2017 Common Programming Errors in CDISC data Sergiy Sirichenko source 9
PharmaSUG 2017 Converting Non-Imputed Dates for SDTM Data Sets With PROC FCMP Noory Y. Kim source 12
PharmaSUG 2017 Gray Areas of ADaM and Ways to Provide Color [presentation] Susan J. Kenny,
Sandra Minjoe,
Nate Freimark,
Jack Shostak,
John K. Troxell,
Deborah J. Bauer		 source 38
PharmaSUG 2017 How to define Treatment Emergent Adverse Event (TEAE) in crossover clinical trials Mengya Yin,
Wen Tan		 source 6
PharmaSUG 2017 Implementing Patient Report Outcome data in clinical trials analysis Qi Wang source 7
PharmaSUG 2017 Multiple Imputation: A Statistical Programming Story Christopher J. Smith,
Scott Kosten		 source 16
PharmaSUG 2017 Population PK/PD Analysis - SAS with R and NONMEM Make Customization Easy Sharmeen Reza source 11
PharmaSUG 2017 Programming Efficiency in the Creation of ADaM BDS Datasets Ellen (Zihui) Lin source 11
PharmaSUG 2017 SAS and ISO8601: A practical approach Derek P. Morgan source 10
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 Time Series Analysis and Forecasting in SAS University Edition Christopher Battiston source 10
SGF 2017 Using SAS to Analyze Emergency Department Visits: Medicaid Patients Compared to Other Pay Sources Bradley Casselman,
Taylor K. Larkin,
Denise J. McManus		 source 16
SESUG 2016 Analyzing non-normal data with categorical response variables Niloofar Ramezani,
Allameh Tabataba'i		 source 9
SESUG 2016 Converting Non-Imputed Partial Dates (for SDTM Data Sets) Using PROC FCMP Noory Y. Kim source 8
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 SDTM What? ADaM Who? A Programmer's Introduction to CDISC Venita DePuy source 9
PHUSE EU 2016 Automating ADaM Helen Hart,
Christof Binder		 source
presentation source		 3
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
WUSS 2016 Building Better ADaM Datasets Faster With If-Less Programming Lei Zhang source 17
WUSS 2016 MIghty PROC MI to the rescue! Cynthia Alvarez source 10
WUSS 2016 Ushering SAS Emergency Medicine into the 21st Century: Toward Exception Handling Objectives, Actions, Outcomes, and Comms Troy Martin Hughes source 23
PharmaSUG 2016 Trivial Date Tasks? PROC FCMP Can Help Jueru Fan source 10
SGF 2016 Analyzing Non-Normal Binomial and Categorical Response Variables under Varying Data Conditions Niloofar Ramezani source 16
SGF 2016 Debt Collection through the Lens of SAS Analytics Karush Jaggi,
Harold Dickerson,
Thomas Waldschmidt		 source
presentation		 11
SGF 2016 Mass-Scale, Automated Machine Learning and Model Deployment Using SAS Factory Miner and SAS Decision Manager Jonathan Wexler,
Steve Sparano		 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 SAS Macro for Generalized Method of Moments Estimation for Longitudinal Data with Time-Dependent Covariates Katherine Cai,
Jeffrey Wilson		 source 9
SGF 2016 Survey Data Imputation with PROC SURVEYIMPUTE Pushpal K. Mukhopadhyay source 26
MWSUG 2015 Debt Collection Through SAS Analytics Lens Karush Jaggi,
Thomas Waldschmidt,
Harold Dickerson,
Eric Hayes,
Goutam Chakraborty		 11
MWSUG 2015 Multiple Imputation for Arbitrary Missing Data: SAS and R Kaushal Chaudhary,
Deanna Naomi Schreiber-Gregory		 7
MWSUG 2015 Ushering SAS Emergency Medicine into the 21st Century: Toward Exception Handling Objectives, Actions, Outcomes, and Comms Troy Martin Hughes 23
PHUSE EU 2015 Errors Beyond Open CDISC Validator Gayatri Karkera,
Nehan Mohan		 source
presentation source		 4
PHUSE EU 2015 Facing Your Fear of the Unknown - Handling Missing Data Amanda Reilly source
presentation source		 7
SESUG 2015 A SAS Algorithm for Imputing Discrete Missing Outcomes Based on Minimum Distance Minimum Distance source 7
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
WUSS 2015 SAS/STAT 14.1: Methods for Massive, Missing, or Multifaceted Data Maura E. Stokes source 25
PharmaSUG CN 2015 A proposal for intervention and event partial date time imputation [presentation] Zhao Chunpeng 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 Hands-On ADaM ADAE Development Sandra Minjoe 7
PharmaSUG CN 2015 Tips for efficient CDISC eCRT production Lanting Li,
Yu Zhu,
Yuejuan Meng,
Huan Zhu		 11
PharmaSUG 2015 It Depends On Your Analysis Need Sandra Minjoe source 10
PharmaSUG 2015 Leveraging ADaM Principles to Make Analysis Database and Table Programming More Efficient Andrew L Hulme source 13
PharmaSUG 2015 Missing Data For Repeated Measures: Single Imputation VS Multiple Imputation Giulia Tonini,
Simona Scartoni,
Camilla Paoli,
Andrea Nizzardo,
Angela Capriati		 source 10
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 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 Multiple Imputation Using the Fully Conditional Specification Method: A Comparison of SAS, Stata, IVEware, and R Patricia A. Berglund source 17
SGF 2015 SAS/STAT 14.1: Methods for Massive, Missing, or Multifaceted Data Maura E. Stokes,
Bob Rodriguez		 source 25
SGF 2015 Statistical Analysis of Publicly Released Survey Data with SAS/STAT Software SURVEY Procedures Donna Brogan source 20
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
SESUG 2014 SDTM What? ADaM Who? A Programmer's Introduction to CDISC Venita DePuy source 9
PHUSE EU 2014 Challenges with the Interpretation of CDIOSC - Who Can We Trust? Linda Palm Simonsson source
presentation source		 10
PHUSE EU 2014 Multiple Imputation: Better than Single Imputation in Pain Studies Ashik Chowdhury source
presentation source		 12
PHUSE EU 2014 NONMEM - A Programmers Point of View Raghu Kishor Koukuntla source
presentation source		 12
MWSUG 2014 Using the time-closest non-missing value to replace the missing value Yubo Gao 4
PharmaSUG 2014 A Guide to the ADaM Basic Data Structure for Dataset Designers Michelle Barrick,
John K. Troxell		 source 12
PharmaSUG 2014 A SAS Macro to address PK timing variables issues Timothy J. Harrington source 14
PharmaSUG 2014 An ADaM Interim Dataset for Time-to-Event Analysis Needs Tom Santopoli,
Kimberly Minkalis,
Sandra Minjoe		 source 9
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 Creating Define-XML version 2 with the SAS Clinical Standards Toolkit 1.6 Lex Jansen source 33
PharmaSUG 2014 Handling with missing data in clinical trials for time-to-event variables Giulia Tonini,
Simona Scartoni,
Angela Capriati		 source 9
PharmaSUG 2014 Hands-On ADaM ADAE Development Sandra Minjoe,
Kimberly Minkalis		 source 7
PharmaSUG 2014 Times Can Be Tough: Taming DATE, TIME and DATETIME Variables Sajeet Pavate source 6
PharmaSUG 2014 Tips to Manipulate the Partial Dates Deli Wang,
Chunxia Lin		 source 4
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 Missing Data: Overview, Likelihood, Weighted Estimating Equations, and Multiple Imputation [presentation] Geert Molenberghs source 56
SGF 2014 SAS Data Mining for Predictor Identification: Developing Strategies for High School Dropout Prevention Wendy B. Dickinson,
Morgan C. Wang		 source 9
SGF 2014 SAS/STAT 13.1 Round-Up Bob Rodriguez,
Maura E. Stokes		 source 25
SGF 2014 Sensitivity Analysis in Multiple Imputation for Missing Data Yang Yuan source 12
SGF 2014 Tying It All Together: A Story of Size Optimization at DSW [presentation] Linda Canada source 61
WUSS 2013 Tools for Imputing Missing Data Taylor Lewis source 16
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 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 Datacut Strategies: What, Why & How Hiren Naygandhi source
presentation source		 6
MWSUG 2013 Adventures in Path Analysis and Preparatory Analysis Brandy R. Sinco,
Phillip L. Chapman		 19
NESUG 2013 The ADaM Solutions to Non-endpoints Analyses Chengxin Li 12
PharmaSUG 2013 Combining Analysis Results from Multiply Imputed Categorical Data Bohdana Ratitch,
Ilya Lipkovich,
Michael O'Kelly		 source 19
PharmaSUG 2013 Designing and Tuning ADaM Datasets Songhui Zhu source 9
PharmaSUG 2013 Hands-On ADaM ADAE Development Sandra Minjoe source 7
PharmaSUG 2013 Traceability in the ADaM Standard Ed Lombardi source 8
PharmaSUG 2013 Using the SAS Clinical Standards Toolkit 1.5 to Import CDISC ODM Files Lex Jansen source 28
SGF 2013 A Flexible Method to Apply Multiple Imputation Using SAS/IML Studio Lisa M. Lix,
Xue Yao		 source
pdf		 9
SGF 2013 A Metadata-Driven Programming Technique Using SAS Xiyun Wang source 14
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 Adding New Rows in the ADaM Basic Data Structure: When and How Mario Widel,
Sandra Minjoe		 source 12
SGF 2013 Maximizing the Power of Hash Tables David J. Corliss source 8
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
SCSUG 2012 POSTER: ADaM Implications of the CDER Data Standards Common Issues Document Sandra Minjoe source 6
PHUSE EU 2012 Interpreting ADaM Standards with OpenCDISC Trupti Bal,
Madhura Paranjape		 source
presentation source		 8
PHUSE EU 2012 Parsing ISO 8601 Standard Partial Dates using Perl Regular Expressions Mark Crangle source
presentation source		 8
PHUSE EU 2012 The 3 Year Obsession: Length of Experiences the Sole Determinant of Competence? Meghana Bhagwat,
Manjusha Gode		 source
presentation source		 9
PHUSE EU 2012 Training Statistical Programmers on SAP Review Skills Sascha Ahrweiler source
presentation source		 7
SESUG 2012 Multiple Imputation for Ordinal Variables: A Comparison of SUDAAN's PROC IMPUTE vs. PROC MI Multiple Imputation source 12
MWSUG 2012 Customer Sentiment Imputation Using Hash Tables David J. Corliss 8
WUSS 2012 Developing Integrated Summary of Safety Database using CDISC Standards Rajkumar Sharma source 7
WUSS 2012 Survival Analysis with PHREG: Using MI and MIANALYZE to Accommodate Missing Data Arthur L. Carpenter,
Christopher F. Ake		 source 7
PharmaSUG 2012 A Complex ADaM dataset? Three different ways to create one. Kevin Lee source 11
PharmaSUG 2012 ADaM Implications from the "CDER Data Standards Common Issues" and SDTM Amendment 1 Documents Sandra Minjoe source 6
PharmaSUG 2012 Creating an Integrated Summary of Safety Database using CDISC ADaM : Challenges, Tips and Things to Watch Out Rajkumar Sharma source 7
PharmaSUG 2012 Identifying and Verifying Treatment-Emergent Adverse Events and Concomitant Medications by Date Specificity Tom Santopoli source 5
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
SGF 2012 Handling Missing Data by Maximum Likelihood Paul D. Allison source 21
SGF 2012 Knowledge (of Your Missing Data) Is Power: Handling Missing Values in Your SAS Data Set Theresa Schwartz,
Rachel Zeig-Owens		 source 8
SESUG 2011 Creating Correlated Variable Tables Dynamically John Barrow,
Aref Dajani		 source 8
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 Using SAS to Examine Aging Expectation (ERA-38) for Older Adults Abbas S. Tavakoli,
Julie Freelove-Charton		 source 8
PHUSE EU 2011 Creating ADaM Friendly Analysis Data from SDTM Using Meta Data Erik Brun,
Rico Schiller		 source
presentation source		 6
PHUSE EU 2011 SDTM Domain QS: Challenges and Pitfalls - an Interpretation Guide to the Implementation Guide Knut Mueller source
presentation source		 6
PHUSE EU 2011 Training Statistical Programmers on SAP Review Skills Sascha Ahrweiler source
presentation source		 7
PHUSE EU 2011 Using the SAS Clinical Standards Toolkit to work with the CDISC ODM model Lex Jansen source
presentation source		 20
MWSUG 2011 Regression calibration with multiple imputations for red blood cell fatty acids James V. Pottala 8
NESUG 2011 Weighted Sehuential Hot Deck Imputation: SAS Macro vs. SUDAANes Proc Hotdeck David Izrael,
Michael P. Battaglia		 7
PharmaSUG 2011 Implementation of Pattern-Mixture Models Using Standard SAS/STAT Procedures Bohdana Ratitch,
Michael O'Kelly		 source 10
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
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 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 Time Series Data Mining with SAS Enterprise Miner Sascha Schubert,
Taiyeong Lee		 source 12
NESUG 2010 A Hercules of Programming - What is A Central Macro Library and Why John He 6
NESUG 2010 Date Conversions in SDTM and ADaM Datasets Lily Zhang,
Lili Chen,
Huei-Ling Chen		 8
WUSS 2010 Survival Analysis with PHREG: Using MI and MIANALYZE to Accommodate Missing Data Christopher F. Ake source 7
PHUSE EU 2010 Define.xml - Tips and Techniques for Creating CRT-DDS Julie A. Maddox,
Mark Lambrecht		 source
presentation source		 10
PHUSE EU 2010 How to go From an SDTM Findings Domain to an ADaM-Compliant Analysis Dataset: An Example Carl Herremans source
presentation source		 8
MWSUG 2010 An Introduction to Multiple Imputation of Complex Sample Data Using SAS 9.2 Patricia A. Berglund 12
MWSUG 2010 Power to Detect Therapist Effects in Randomized, Controlled Trials of Interventions in Clinical Psychology Douglas Thompson,
Fary Cachelin,
Ruth Striegel-Moore,
Terry Wilson		 16
MWSUG 2010 Survival Analysis with PHREG: Using MI and MIANALYZE to Accommodate Missing Data Christopher F. Ake,
Arthur L. Carpenter		 6
SESUG 2010 Potential Change in Reliability Measures Based on Decreased Sample Size for the Census Coverage Measurement Survey Vincent Thomas Mule source 12
SESUG 2010 Using SAS to Examine Missing Data in Psychometric Research Abbas S. Tavakoli,
Elizabeth Register		 source 12
PharmaSUG 2010 From SAP to ADaM: The Nuts and Bolts Nancy Brucken,
Paul K. Slagle		 9
PharmaSUG 2010 The new SAS 9.2 FCMP Procedure, what functions are in your future? John H. Adams,
Boehringer Ingelheim		 11
SGF 2010 An Introduction to Multiple Imputation of Complex Sample Data Using SAS 9.2 Patricia A. Berglund source 12
SGF 2010 Analyzing Interval-Censored Survival Data with SAS Ying C. So,
Gordon Johnston		 source 14
SGF 2010 SAS One-liners Stephen Hunt source 6
SESUG 2009 Imputation for Censored Observations in Survival Studies Allowing for a Positive Cure Rate Alan B. Cantor source 6
SESUG 2009 Multiple Imputation for Survey Data Analysis Yeats Ye source 7
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 SAS Text Miner: Introduction and Case Study Maria Skaletsky,
Dominique Haughton		 5
PharmaSUG 2009 SAS 1-liners Stephen Hunt 6
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
SESUG 2008 A macro for nearest neighbor imputation Lung-Chang Chien source 6
PHUSE EU 2008 Implementing CDISC at Boehringer Ingelheim Michael Knoessl,
Peter Leister		 source 5
MWSUG 2008 Imputation of Categorical Missing Data: A comparison of Multivariate Normal and Multinomial Methods Holmes Finch,
Matt Margraf		 12
PNWSUG 2008 Applied Aspects of Using Multiple Imputation in Clinical Data Christina Gullion 7
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
SGF 2008 Data Mining Application of Non-Linear Mixed Modeling in Water Quality Analysis Anpalaki J. Ragavan source 14
SGF 2008 The Early Warning Project: Prewarning and Avoiding Problems and Costly Downtime in Complex Industrial Processes Torulf Mollestad,
Frode Oplenskedal		 source 12
NESUG 2007 A Consolidated Macro for Iterative Hot Deck Imputation Bruce Ellis 15
NESUG 2007 Implementing Multiple Imputation in an Automatic Variable Selection Scenario Krishna Mehta,
Mayank Rustagi,
Saurabh Kohli,
Siddharth Tiwari		 4
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 Data Mining Using SAS Enterprise Miner Randall Matignon source 10
WUSS 2007 Time Series Cross Sectional Analysis of Total Nitrogen Concentration in Truckee River, Nevada Using SAS Anpalaki J. Ragavan source 11
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 Implementing Worst Rank Imputation Using SAS Qian Wang,
Eric Qi		 10
PharmaSUG 2007 SAS Markov Chain Monte Carlo (MCMC) Simulation in Practice Shi-Tao Yeh,
Scott D Patterson		 20
SGF 2007 Identifying and Overcoming Common Data Mining Mistakes Doug Wielenga source 20
SGF 2007 Population Pharmacokinetics Studies with Nonlinear Mixed Effects Modeling Zaizai Shen,
David Lu		 source 11
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
PNWSUG 2006 Missing Data? A Look at Two Imputation Methods Anita Rocha 2
SESUG 2006 Combining PROC GENMOD Models with Multinomial Outcomes Using PROC MIANALYZE Marie Gantz source 7
SESUG 2006 Creating RTF Tables with Univariate Analyses of Multiply Imputed Data Marie Gantz source 14
NESUG 2006 How To Produce Almost Perfect RTF Output Suzanne M. Dorinski 20
NESUG 2006 Imputation of Missing Date Information for Injuries and Poisonings Reported in the National Health Interview Survey Richard Coles,
Patricia Barnes,
Lois Fingerhut,
Jane Gentleman,
Nathaniel Schenker,
Margaret Warner		 7
PharmaSUG 2006 Imputing ISO8601 Dates From Character Variables Containing Partial Dates John R. Gerlach 4
PharmaSUG 2006 Systematic Inductive Method for Imputing Partial Missing Dates in Clinical Trials Ping Liu 8
SESUG 2005 _05 Managing a Many to Many Merge using Formats, Direct Access, and Implied Hashing source 8
SESUG 2005 The Missing Link: Data Analysis with Missing Information Venita DePuy source 9
WUSS 2005 A Comparison of Missing Data Handling Methods Catherine Truxillo source 20
WUSS 2005 A Macro Mapping Date and Time Variable to CDISC Date and Time Variable Song Liu,
Brett Sellars		 source 5
WUSS 2005 Effects of PROC EXPAND Data Interpolation on Time Series Modeling when the Data are Volatile or Complex Keiko I. Powers source 7
PharmaSUG 2005 RETAIN or NOT? Is LAG Far Behind? Venky Chakravarthy 9
SUGI 2005 Imputation of Categorical Variables with PROC MI Paul D. Allison source 14
SUGI 2005 Maximum Likelihood Parameter Estimation with Incomplete Data Catherine Truxillo source 19
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
WUSS 2004 An Approach to Record-Linkage Using Propensity Score Paul D. Frederick source 12
PNWSUG 2004 Dynamically Change Your Perspective with the Link King (and a Little SAS/AF) Kevin M. Campbell 9
PharmaSUG 2004 A Simplified Way to Create Flags for the Three-Dimensional Prior, Concomitant, and Post Medication Classification Aileen L. Yam 5
PharmaSUG 2004 Incremental Methods of Imputation for Longitudinal Data with Informative Missingness Naum M. Khutoryansky 4
SUGI 2004 An Approach to Record-Linkage Using Propensity Score Paul D. Frederick source 13
SUGI 2004 Model-Based Multiple Imputation Geoffrey Paulin,
Shirley Tsai,
Melissa Grance		 source 13
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
SESUG 2003 An Analysis Tool for Reviewing Farm Economic Data Using SAS/AF Van Johnson source 7
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 What is Missing in SAS? Imelda C. Go source 5
NESUG 2003 PROC MI as the Basis for a Macro for the Study of Patterns of Missing Data Carl E. Pierchala 8
PharmaSUG 2003 Parameter estimation for incomplete bivariate longitudinal data in clinical trials Naum M. Khutoryansky 6
SUGI 2003 An Alternative to PROC MI for Large Samples David T. Lanning,
Doug Berry		 source 3
NESUG 2002 Hot-Deck Imputation: A Simple DATA Step Approach Lawrence W. Altmayer 8
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
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 Imputation Strategies for Sexual Orientation Using SAS PROC MI Jesse Albert Canchola,
Joe Catania,
Torsten Neilands		 source 6
WUSS 2002 Survival Analysis with PHREG: Using MI and MIANALYZE to Accommodate Missing Data Arthur L. Carpenter source 6
PharmaSUG 2002 Efficient Interpolation Calculation Julia Z. Zhang 4
PharmaSUG 2002 Imputation of Longitudinal Incomplete Data in Clinical Trials Naum M. Khutoryansky 4
NESUG 2001 INTERPOLATION CALCULATION MADE EZ Julia Z. Zhang,
David C. Chen		 4
NESUG 2001 Optimizing Survey Research from Start to Finish Using SAS Software Lark Lee,
Yvonne Brandreth		 7
VIEWS 2001 The Version 8 Applied Statistician David Shannon 15
SEUGI 2001 How to approach a segmentation project for ten million customers with SAS Enterprise Miner [presentation] Luis Miguel Muruzabal source 26
PharmaSUG 2001 Imputation Techniques Using SAS Software For Incomplete Data In Diabetes Clinical Trials Naum M. Khutoryansky,
Won-Chin Huang		 4
SUGI 2001 Outliers, Inliers, and Just Plain Liars - New Graphical EDA+ Techniques For Understanding Data Dave L. DesJardins source 6
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
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 Multiple Imputation for Missing Data: Concepts and New Development Yang Yuan source 10
SESUG 1999 Hot-Deck Imputation: A Simple DATA Step Approach Lawrence W. Altmayer source 7
NESUG 1999 Get In Step With StEPS: Standard Economic Processing System Deborah Tasky,
Anne Linonis,
Scott Ankers,
Douglas Hallam,
Lawrence W. Altmayer,
Deborah Chew		 8
MWSUG 1998 Development of a Multi-Year Population-Based Data Warehouse of Standardized Medical Costs: Methods and Results Priscilla Van Grevenhof,
Judith L. Wagner,
James M. Naessens,
Charles H. Darby,
Jill M. Killian,
Eva R. Helgeson,
Roger W. Evans		 4
NESUG 1998 Developing a Parameter Evaluation Methodology Using SAS Richard W. Graham 5
NESUG 1998 Developing Ratio Edit Parameters with SAS for the Economic Census Richard W. Graham 10
NESUG 1998 Macro for computing LOCF: Comparison of results with PROC MIXED Patricia R. Houck 2
SESUG 1998 ADJiANCED TlITORIALS Hot-Deck Imputation: A Simple DATA Step Approach Lawrence W. Altmayer 6
SESUG 1998 Developing a Parameter Evaluation Methodology Using SAS Richard W. Graham 5
SESUG 1998 Developing Ratio Edit Parameters with SAS for the Economic Census Richard W. Graham 10
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
NESUG 1997 Hot-Deck Imputation with SAS Arrays and Macros for Large Surveys John Stiller 6
NESUG 1997 Hot-Deck Imputation: A Simple DATA Step Approach Lawrence W. Altmayer 6
NESUG 1997 Missing Value Imputation Methods for Large Databases Leonardo Auslender 8
SUGI 1997 SAS Code Generator Based on Table-Driven Methodology in a Batch Environment Arthur K. Yao source 6
NESUG 1996 Poor Mans's Missing Value Recovery Methods Leonardo Auslender 10
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
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
SUGI 1995 SAS Macros Useful in Imputing Missing Survey Data Barbara Lepidus Carlson,
Brenda Cox,
Linda Bandeh		 source 6
SUGI 1993 Filling in the Blanks: Multiple Imputation for Replacing Missing Values in Survey Data P. M. Wright source 3
SUGI 1993 The SAS System in a Longitudinal Environmental Health Intervention Study with Repeated Monitoring WinKey Pan source 4
MWSUG 1992 The Evolving Role of the SAS System in the Development of the Production System for the Consumer Price Index (CPI) James E. Johnstone,
Stuart Leven		 10
SUGI 1992 The Evolving Role of the SAS System in the Development of the Production System for the Consumer Price Index (CPI) James E. Johnstone,
Stuart Leven		 source 10
SCSUG 1991 Nearest Neighbor Hot-Deck Imputation for Missing Value With SAS/IML Thomas W. Sager,
James P. Gise,
M. W. Hemphill		 9
SUGI 1991 Incorporating Adjustment into Sample Data Products Willard Carl Losinger,
Alfredo Navarro		 source 6
SUGI 1989 A Precursor of Survey Processing in the SAS System at the Census Bureau Robert S. Lohr source 3
SUGI 1988 An Imputation System for a Large-Scale Survey Eugene M. Burns,
Doug Valcour		 source 6
SEUGI 1987 Economically Active Population Estimates and Projections, 1950-2025 B. Binesh Aghevli,
C. Gerlier		 source 16
SUGI 1986 The Use of the SAS System in the Development of the Production System for the Consumer Price Index (CPI) James E. Johnstone,
Merry G. Rabb,
Joshua Sharlin		 source 5
SUGI 1982 Weighted Sequential Hot Deck Imputation Macros Vincent G. Iannacchione source 5
SUGI 1980 A Macro for Multivariate Randomization Analyses of Stratified Sample Data Ingrid A. Amara,
Gary G. Koch		 source 11

Back to keywords