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84 papers and 3 presentations; 83 titles.

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Conference × Year × Title × Authors Extras Pages
PharmaSUG CN 2026 Handling Missing Data in Analgesia Clinical Trials and Corresponding SAS Implementation Jingbo Zhu,
Jiehan Zhang		 source 17
PHUSE APAC 2026 From Missing to Meaningful: Validating Multiple Imputation Techniques for Reliable Analysis Karthick Jayaraman 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
PharmaSUG 2025 Approaches to Developing Multiple Imputation ADaM Datasets Kang Xie source 8
PharmaSUG 2025 Handling Missing Data in External Control Arms: Best Practices, Recommendations, and SAS Code Examples Yutong Zhang source 6
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 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
PHUSE EU 2024 Unveiling the Tipping Point: An In-depth Analysis Varsha Mithun Patil,
Ephicacy Lifescience		 source
presentation source		 8
WUSS 2024 Handling Missing Values in SAS 9 and SAS Viya [presentation] Melodie Rush source 102
PharmaSUG 2024 Implementation of composite estimands for responder analysis based on change from baseline in non-solid tumours Reshma Radhakrishnan source 11
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
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
PHUSE EU 2022 Handling Missing Data in Statistical Programming Using Multiple Imputations in SAS [presentation] Marta Tomczyk,
Martyna Gawlowska		 source
video		 15
PHUSE US 2022 Reassure Your Assumptions Using Sensitivity Analysis [presentation] Vadym Kalinichenko source
video		 29
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
PHUSE US 2020 Handling Missing Data in Clinical Trials: Techniques and Methods Pennidhi Karlakunta,
Naveen Kommuru		 source
presentation source		 9
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 Multiple imputation as a valid way of dealing with missing data Vadym Kalinichenko source
presentation source		 11
PharmaSUG 2019 Experiences in Building CDISC Compliant ADaM Dataset to Support Multiple Imputation Analysis for Clinical Trials Xiangchen (Bob) Cui source 20
PharmaSUG 2019 SAS V9.4 MNAR statement for multiple imputations for missing not at random in longitudinal clinical trials Lingling Li source 11
SGF 2019 Multiple Imputation for Skewed Multivariate Data: A Marriage of the MI and COPULA Procedures Zhixin Lun,
Ravindra Khattree		 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
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
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
PharmaSUG 2017 Multiple Imputation: A Statistical Programming Story Christopher J. Smith,
Scott Kosten		 source 16
SESUG 2016 An Easy Route to a Missing Data Report with ODS + PROC FREQ + A Data Step Michael S. Zdeb source 10
SESUG 2016 Analyzing non-normal data with categorical response variables Niloofar Ramezani,
Allameh Tabataba'i		 source 9
MWSUG 2016 Time is of Essence: The power of "MISS" is "NOT being missed"! Gowri Madhavan,
Brittney Delev		 11
WUSS 2016 MIghty PROC MI to the rescue! Cynthia Alvarez source 10
MWSUG 2015 Multiple Imputation for Arbitrary Missing Data: SAS and R Kaushal Chaudhary,
Deanna Naomi Schreiber-Gregory		 7
PHUSE EU 2015 Facing Your Fear of the Unknown - Handling Missing Data Amanda Reilly source
presentation 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
PharmaSUG CN 2015 Clinical trials optimization: Monte Carlo Simulation modeling and SAS applications Ye Meng 5
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 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
PHUSE EU 2014 Multiple Imputation: Better than Single Imputation in Pain Studies Ashik Chowdhury source
presentation source		 12
PharmaSUG 2014 Handling with missing data in clinical trials for time-to-event variables Giulia Tonini,
Simona Scartoni,
Angela Capriati		 source 9
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
PharmaSUG 2013 Combining Analysis Results from Multiply Imputed Categorical Data Bohdana Ratitch,
Ilya Lipkovich,
Michael O'Kelly		 source 19
SGF 2013 A Flexible Method to Apply Multiple Imputation Using SAS/IML Studio Lisa M. Lix,
Xue Yao		 source
pdf		 9
SGF 2013 A SAS Macro for Applying Multiple Imputation to Multilevel Data Stephen Mistler source 8
SESUG 2012 Multiple Imputation for Ordinal Variables: A Comparison of SUDAAN's PROC IMPUTE vs. PROC MI Multiple Imputation source 12
WUSS 2012 Survival Analysis with PHREG: Using MI and MIANALYZE to Accommodate Missing Data Arthur L. Carpenter,
Christopher F. Ake		 source 7
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
NESUG 2011 An Easy Route to a Missing Data Report with ODS + PROC FREQ + A Data Step Michael S. Zdeb 10
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
NESUG 2010 Know What You Are Missing: How to Catalogue and Manage Missing Pieces of Historical Data Shankar Yaddanapudi 5
WUSS 2010 Survival Analysis with PHREG: Using MI and MIANALYZE to Accommodate Missing Data Christopher F. Ake source 7
MWSUG 2010 An Introduction to Multiple Imputation of Complex Sample Data Using SAS 9.2 Patricia A. Berglund 12
MWSUG 2010 Survival Analysis with PHREG: Using MI and MIANALYZE to Accommodate Missing Data Christopher F. Ake,
Arthur L. Carpenter		 6
SGF 2010 An Introduction to Multiple Imputation of Complex Sample Data Using SAS 9.2 Patricia A. Berglund source 12
SESUG 2009 Multiple Imputation for Survey Data Analysis Yeats Ye source 7
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
PNWSUG 2008 Applied Aspects of Using Multiple Imputation in Clinical Data Christina Gullion 7
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
NESUG 2007 Implementing Multiple Imputation in an Automatic Variable Selection Scenario Krishna Mehta,
Mayank Rustagi,
Saurabh Kohli,
Siddharth Tiwari		 4
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 SAS Markov Chain Monte Carlo (MCMC) Simulation in Practice Shi-Tao Yeh,
Scott D Patterson		 20
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
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 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
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
SESUG 2002 Handling Missing Data with Multiple Imputation Using PROC MI in SAS Hossein N. Yarandi source 5
WUSS 2002 Imputation Strategies for Sexual Orientation Using SAS PROC MI Jesse Albert Canchola,
Joe Catania,
Torsten Neilands		 source 6
VIEWS 2001 The Version 8 Applied Statistician David Shannon 15
PharmaSUG 2001 Imputation Techniques Using SAS Software For Incomplete Data In Diabetes Clinical Trials Naum M. Khutoryansky,
Won-Chin Huang		 4
SUGI 2000 Multiple Imputation for Missing Data: Concepts and New Development Yang Yuan source 10

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