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multiple imputation

124 papers and 11 presentations; 131 titles.

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Conference × Year × Title × Authors Extras Pages
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
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 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 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 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 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 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
MWSUG 2025 Mind the Gaps: Automating Multiple Imputation in Clinical Trial Workflows [presentation] Crisa Chen,
Jiangang Cai		 source 30
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 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 Estimands: How to Make ADaM Implementation Your Friend Jaclyn Amberg source
presentation source		 10
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
WUSS 2024 Multiple Imputation in Interim Analyses [presentation] Bill Coar source 29
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 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
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 Efficacy Analyses with Imputations: Methods, Limitations and Lessons Learned Amine Khemiri source
presentation source		 3
SESUG 2023 CAMIS: Comparing Analysis Method Implementations in Software Brian K. Varney source 4
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 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 US 2023 Performing Propensity-score-integrated Comparative Analysis in External Control Arm Studies Using R [presentation] Lixiao Chen,
Lorraine Fang		 source
video		 20
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 Pan-pharma Industry Collaboration - New Horizon, Success Stories [presentation] Tadeusz Lewandowski source
video		 21
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
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 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
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
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
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 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
SGF 2019 SCORE! Techniques for Scoring Predictive Regression Models Using SAS/STAT Software Phil Gibbs,
Randy Tobias		 source 18
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
PharmaSUG 2017 Combining Survival Analysis Results after Multiple Imputation of Censored Event Times Jonathan L Moscovici,
Bohdana Ratitch		 source 11
PharmaSUG 2017 Multiple Imputation: A Statistical Programming Story Christopher J. Smith,
Scott Kosten		 source 16
SESUG 2016 Analyzing non-normal data with categorical response variables Niloofar Ramezani,
Allameh Tabataba'i		 source 9
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
WUSS 2016 MIghty PROC MI to the rescue! Cynthia Alvarez source 10
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
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 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
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
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
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/STAT 13.1 Round-Up Bob Rodriguez,
Maura E. Stokes		 source 25
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
MWSUG 2013 Adventures in Path Analysis and Preparatory Analysis Brandy R. Sinco,
Phillip L. Chapman		 19
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
SGF 2013 A SAS Macro for Computing Pooled Likelihood Ratio Tests with Multiply Imputed Data Stephen Mistler source 10
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 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
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
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
MWSUG 2011 Regression calibration with multiple imputations for red blood cell fatty acids James V. Pottala 8
PharmaSUG 2011 Implementation of Pattern-Mixture Models Using Standard SAS/STAT Procedures Bohdana Ratitch,
Michael O'Kelly		 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
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 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 Using SAS to Examine Missing Data in Psychometric Research Abbas S. Tavakoli,
Elizabeth Register		 source 12
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
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
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
PNWSUG 2006 Missing Data? A Look at Two Imputation Methods Anita Rocha 2
SESUG 2006 Creating RTF Tables with Univariate Analyses of Multiply Imputed Data Marie Gantz source 14
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 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
PharmaSUG 2004 Incremental Methods of Imputation for Longitudinal Data with Informative Missingness Naum M. Khutoryansky 4
SUGI 2004 Model-Based Multiple Imputation Geoffrey Paulin,
Shirley Tsai,
Melissa Grance		 source 13
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
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
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
WUSS 2002 Survival Analysis with PHREG: Using MI and MIANALYZE to Accommodate Missing Data Arthur L. Carpenter source 6
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
NESUG 1995 HOTROD: An Intelligent Hotdeck Imputation System Eugene M. Burns,
Jay Olsen		 8
SUGI 1993 Filling in the Blanks: Multiple Imputation for Replacing Missing Values in Survey Data P. M. Wright source 3

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