HOME QUICK SEARCH AUTHORS KEYWORDS HELP BUGS

proc mianalyze

44 papers; 42 titles.

Back to keywords

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 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 Multiple Imputations Overview of Missing Data Problem, Solution Methods and SAS Implementation Dmytro Kalinin source
presentation source		 13
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 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
SGF 2020 Analyzing Non-normal Data: Application to Missing Data Problems Niloofar Ramezani source 10
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 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
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 Survey Data Imputation with PROC SURVEYIMPUTE Pushpal K. Mukhopadhyay source 26
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 2014 Combining Type-III Analyses from Multiple Imputations Binhuan Wang,
Yixin Fang,
Man Jin		 source 6
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
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 Computing Pooled Likelihood Ratio Tests with Multiply Imputed Data Stephen Mistler source 10
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
PharmaSUG 2011 Implementation of Pattern-Mixture Models Using Standard SAS/STAT Procedures Bohdana Ratitch,
Michael O'Kelly		 source 10
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
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
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
SESUG 2005 The Missing Link: Data Analysis with Missing Information Venita DePuy source 9
SUGI 2005 Imputation of Categorical Variables with PROC MI Paul D. Allison source 14
SUGI 2005 Rounding after Multiple Imputation with Non-Binary Categorical Covariates Christopher F. Ake source 11
SUGI 2004 Using SAS Procedures to Make Sense of a Complex Food Store Survey Jeffrey M. Gossett,
Pippa M. Simpson,
Renée A. Hall,
Carol Connell,
Kathy Yadrick,
Margaret L. Bogle		 source 8
SUGI 2000 Multiple Imputation for Missing Data: Concepts and New Development Yang Yuan source 10

Back to keywords