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propensity score

131 papers and 6 presentations; 127 titles.

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Conference × Year × Title × Authors Extras Pages
PharmaSUG 2026 Construction and Evaluation of External Controls Using Propensity Score Methods Bala Niharika Pillalamarri,
Yayu Li,
Hongbing Jin		 source 14
PharmaSUG 2026 Contrast Effects in Curated Observational Data Shankar Srinivasan,
Helen Guo,
Yvonne Buttkewitz		 source 16
PharmaSUG 2026 Embracing Novel Approaches to Automated Causal Inference Framework Laura Watson,
Sherrine Eid		 source 15
PharmaSUG 2026 PSMATCH: Propensity Score Matching of Clinical Trial Data with External Control Arms Ginger Barlow source 8
PharmaSUG 2026 Streamlining Workflows in Real-World Evidence Studies with an R-Based Automation Tool Lihai Song source 16
PHUSE US 2026 A Practical Approach to Causal Inference Sherrine Eid,
Laura Watson		 source
presentation source		 10
PHUSE US 2026 Outcome Blinding in External Control Arm Construction Using RWD: Challenges and Best Practices Peishan(Jenny) Han,
Lixiao Chen		 source
presentation source		 7
PHUSE US 2026 Robust Causal Inference in Real-World Evidence Studies with Double Machine Learning Yilun Huang,
Jing Dai		 source
presentation source		 5
PHUSE US 2026 The Curious Case of External Controlled Arms (ECAs): Practical Solutions for External and RWD Integration Manish Deole,
Preethi Samala		 source
presentation source		 6
PHUSE APAC 2026 Enhancing Interpretation of Propensity Score Matching through Advanced Data Visualization in Real-World Studies KiranKumar Vyas,
Umayal Annamalai		 source
presentation source		 7
PHUSE APAC 2026 Estimation of the Hypothetical Estimands using Longitudinal version of the TMLE Shreya Wahi source
presentation source		 8
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 Leveraging Real-World Evidence (RWE) in Clinical Trial Design: Statistical Challenges and Opportunities Muhammed Farzan K source
presentation source		 6
PHUSE APAC 2026 One-year Urate-Lowering Therapy Adherence and Risk of Major Osteoporotic Fracture: A Target Trial Emulation Study Seong Jun Byun source
poster source		 5
PHUSE APAC 2026 Targeted Maximum Likelihood Estimation: A Unified Approach for Confounding and Selection Bias Mahjoob O. Majeed,
Noel S. John		 source
presentation source		 6
PHUSE EU 2025 Evaluating the Use of Real-World Data (RWD) as a Synthetic Control Group: A Statistical Approach Vasiliki Economou,
Dimitrios Kounelis		 source
poster source		 17
PHUSE EU 2025 Inverse Probability Weighting in Multiple Groups: Balancing Baseline Characteristics and Improving Precision of Average Treatment Effect Estimation Dimitris Karletsos source
presentation source		 11
PharmaSUG 2025 Practical Process in SAS of Using External Controls Hui Mao,
Na Wang,
Lixin Gao		 source 5
SESUG 2024 Using PROC PSMATCH for Assembling Parallel Test Forms Tsung-Hsun Tsai,
Yung-Chen Hsu		 source 11
PharmaSUG 2024 Comparing SAS and R Approaches in Creating Multicell Dot Plots in Statistical Programming Yi Guo source 14
PharmaSUG 2024 Win a PS5! How to Run and Compare Propensity Score Matching Performance Across Multiple Algorithms in Five Minutes or Less Catherine Briggs,
Sherrine Eid,
Samiul Haque,
S. Robert Collins		 source 11
PHUSE US 2024 Comparing the Performance of Propensity Score Matching Algorithms from Multiple Programming Languages Catherine Briggs source
presentation source		 7
PHUSE EU 2023 A Beginner's Guide to Overcome the Attrition Bias in Clinical Research: The Method of Inverse Probability of Attrition Weighting (IPAW) Parichay Srivastava source
presentation source		 9
PharmaSUG 2023 Low Code Approach to Clinical Application Development using SAS Studio Custom Step David Olaleye source 15
PHUSE US 2023 Leveraging Big Data in Real-World Evidence Generation: Data Engineering Challenges and Possibilities Samiul Haque,
S. Robert Collins		 source
presentation source		 6
PHUSE US 2023 Performing Propensity-score-integrated Comparative Analysis in External Control Arm Studies Using R [presentation] Lixiao Chen,
Lorraine Fang		 source
video		 20
WUSS 2022 Study of cause and effect in medical research via SAS Statistical package Oleg Korovyanko source 11
PHUSE US 2021 Using Real-world Data in Supporting Health Authority Submission [presentation] Ruoying Fang,
Shuang Zhang		 source
video		 26
SGF 2021 Modeling Causal Effects Using SAS/STAT John Amrhein,
McDougall Scientific		 source 21
PHUSE EU 2020 How to Limit Bias in Observational Studies Analysis - Propensity Score Matching Versus Logistic Regression Roxane Debrus source
video
presentation source		 12
PHUSE EU 2020 Re-scaling Weights to Effective Sample Size [presentation] Andrea Parodi,
Dimitri Petratchenko		 source
video		 25
SESUG 2020 A Doctor's Dilemma: How Propensity Scores Can Help Control for Selection Bias in Medical Education Deanna Naomi Schreiber-Gregory source 13
SGF 2020 A Doctor's Dilemma: How Propensity Scores Can Help Control for Selection Bias in Medical Education Deanna Naomi Schreiber-Gregory source 13
SGF 2020 Propensity Score Matching with Survey Data Bocheng Jing source 14
SGF 2020 Time After Time: D-I-D and ITS Models in SAS Margaret Warton source 21
PharmaSUG 2020 A Doctor's Dilemma: How Propensity Scores Can Help Control for Selection Bias in Medical Education Deanna Naomi Schreiber-Gregory source 14
SESUG 2019 Introduction to Propensity Score Modeling and Treatment Effect Estimation Jason Brinkley source 13
WUSS 2019 Automating a Summary Report of PSM Model and Match Results Desiree Hall source 9
SGF 2019 Applying Propensity Score Methods to Complex Survey Data Using PROC PSMATCH Patrick Karabon source 10
SGF 2019 Building a Propensity Score Model with SAS/STAT Software: Planning and Practice Michael Lamm,
Clay Thompson,
Yiu-Fai Yung		 source 24
SGF 2019 Integrating Case Studies in a Health Analytics Curriculum Besa Smith,
Tyler C. Smith		 source 21
SGF 2019 Matchy-Matchy: Role of Strategy, Case to Control Ratio, and Number of Variables in PROC PSMATCH [presentation] Brittany Hand source 17
SGF 2019 Real-World Evidence and Population Health Analytics: Intersection and Application David Olaleye source 16
PHUSE US 2019 A SAS Macro for Variable Ratio Treatment vs. Placebo in Propensity Score Matching Punjita Baranwal,
Prashant Kulkarni		 source
presentation source		 13
PHUSE EU 2018 Propensity Score Matching Stephanie Fechtner source
presentation source		 9
MWSUG 2018 Propensity Scores and Causal Inference for (and by) a Beginner Bruce S. Lund 18
WUSS 2018 Oops, I D-I-D It Again! Advanced Difference-in-Differences Models in SAS Margaret Warton,
Melissa Parker		 source 17
SGF 2018 !WRONG PAPER FIEL - WAITING for FIX! Nikki M. Carroll source 9
SGF 2018 A General SAS Macro to Implement Optimal N:1 Propensity Score Matching Within a Maximum Radius Kathy Hardis Fraeman source 7
SGF 2018 Application of Propensity Score Models in Observational Studies Nikki M. Carroll source 9
SGF 2018 Improved Campaigning at Africa's Largest Pay TV Service Provider with SAS Marketing Optimization Jean De Villiers source 16
SGF 2018 SAS Macros for Computing Causal Mediated Effects in Two- and Three-Wave Longitudinal Models David P. MacKinnon,
Matthew Valente		 source 18
SESUG 2017 Propensity Score Methods for Causal Inference with the PSMATCH Procedure Yang Yuan,
Yiu-Fai Yung,
Maura E. Stokes		 source 17
SESUG 2017 Using SAS to Employ Propensity Score Matching in an Institutional Research Office to Create Matched Groups for Outcomes Analyses Bobbie E. Frye,
James E. Bartlett		 source 19
MWSUG 2017 Exploring the Relationship Between Substance Abuse and Dependence Disorders and Discharge Status: Results and Implications Deanna Naomi Schreiber-Gregory 25
WUSS 2017 Propensity Score Methods for Causal Inference with the PSMATCH Procedure Maura E. Stokes source 17
PharmaSUG 2017 Two Roads Diverged in a Narrow Dataset-When Coarsened Exact Matching is More Appropriate than Propensity Score Matching Aran Canes source 7
SGF 2017 A General SAS Macro to Implement Optimal N:1 Propensity Score Matching Within a Maximum Radius Kathy Hardis Fraeman source 13
SGF 2017 A Practical Guide to Getting Started with Propensity Scores Thomas Gant source 10
SGF 2017 Estimating Causal Effects from Observational Data with the CAUSALTRT Procedure Michael Lamm,
Yiu-Fai Yung		 source 18
SGF 2017 Propensity Score Methods for Causal Inference with the PSMATCH Procedure Yang Yuan source 17
SESUG 2016 A General SAS Macro to Implement Optimal N:1 Propensity Score Matching Within a Maximum Radius Matching Within source 11
PHUSE EU 2016 Considerations in the Use of Propensity Scoresin Observational Studies Lawrence Rasouliyan,
Estel Plana		 source
presentation source		 17
PHUSE EU 2016 Proposal of a General Macro Framework for Propensity-score Matching [presentation] Holger Langkabel source 23
WUSS 2016 Selection Bias: How Can Propensity Score Utilization Help Control For It? Deanna Naomi Schreiber-Gregory source 13
PharmaSUG 2016 Making Greedy into Optimal! A Poor Woman's Attempt to Get Optimal Propensity Score Matching from a Greedy Matching Algorithm Janet M. Grubber,
Carl Pieper,
Cathleen Colon-Emeric		 source 12
PharmaSUG 2016 What-s the Case? Applying Different Methods of Conducting Retrospective Case/Control Experiments in Pharmacy Analytics Aran Canes source 8
SGF 2016 Integrating SAS and R to Perform Optimal Propensity Score Matching Lucy D'Agostino McGowan,
Robert Greevy		 source 11
SGF 2016 Make Predictions More Predictive: Lift Retail Marketing Campaign Response by Blending Logistic Regression and the Cox Proportional Hazards Model Hsin-Yi Wang source 10
SGF 2016 Using Administrative Databases for Research: Propensity Score Methods to Adjust for Bias Ruth Croxford source 10
SGF 2016 Using Propensity Score Analyses to Adjust for Selection Bias: A Study of Adolescent Mental Illness and Substance Use Deanna Naomi Schreiber-Gregory source 7
MWSUG 2015 A Simple Adjustment for Selection Bias Through Use of Propensity Scoring Deanna Naomi Schreiber-Gregory 24
MWSUG 2015 Utilizing Propensity Score Analyses to Adjust for Selection Bias: A Study of Adolescent Mental Illness and Substance Use Deanna Naomi Schreiber-Gregory 7
PHUSE EU 2015 Bias Reduction in Observational Cohort Studies - Obtaining Matched Cohorts Using Propensity Scores Shelley Fordred,
Victor Kiri		 source
presentation source		 8
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 Using Bootstrapping to Evaluate LOS Median Differences between Two Knee Arthroplasty Procedures Yubo Gao source 6
PharmaSUG 2015 A General SAS Macro to Implement Optimal N:1 Propensity Score Matching Within a Maximum Radius Kathy Hardis Fraeman source 12
PharmaSUG 2015 Distributed data networks: A paradigm shift in data sharing and healthcare analytics Jennifer R. Popovic source 12
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 Causal Analytics: Testing, Targeting, and Tweaking to Improve Outcomes Jason Pieratt source 8
SGF 2015 Random vs. Fixed Effects: Which Technique More Effectively Addresses Selection Bias in Observational Studies Jessica Navratil-Strawn,
Stephen Hartley,
Ronald J. Ozminkowski		 source
zip		 8
SGF 2015 Reducing the Bias: Practical Application of Propensity Score Matching in Healthcare Program Evaluation Amber Schmitz,
Jessica Navratil-Strawn,
Stephen Hartley,
Ronald J. Ozminkowski		 source 7
PHUSE EU 2014 Simple and Efficient Matching Algorithms for Case-Control Matching Berber Snoeijer,
Edith Heintjes		 source
presentation source		 6
MWSUG 2014 Comparing regression, propensity matching and coarsened exact matching in healthcare observational studies using SAS: An example from the Medical Expenditure Panel Survey (MEPS) Douglas Thompson 16
MWSUG 2014 Reducing the Bias: Practical Application of Propensity Score Matching in Healthcare Program Evaluation Amber Schmitz,
Jessica Navratil-Strawn,
Stephen Hartley,
Ronald J. Ozminkowski		 7
PharmaSUG 2014 A SAS Macro to Evaluate Balance after Propensity Score Matching Erin Hulbert,
Lee Brekke		 source 12
PharmaSUG 2014 Methodology for Non-Randomized Clinical Trials: Propensity Score Analysis Dan Conroy source 5
SGF 2014 Case Control Matching: Comparing Simple Distance- and Propensity Score-Based Methods Lovedeep Gondara,
Colleen E. McGahan		 source 7
WUSS 2013 Estimating the Impact of Lost to Follow-up on Breast Cancer Patients' Disease-free Survival Debbie Yan Qun Huang source 7
WUSS 2013 Matching with Hash Objects Gregory Watson source 6
NESUG 2013 Understanding the outliers in healthcare expenditure data Gandhi Bhattarai 15
SGF 2013 A Case Application of Propensity Score Matching in MTM Outcomes Evaluation at Retail Pharmacy Michael Taitel,
Youbei Lou,
Zhongwen Huang		 source
pdf		 11
SGF 2013 Propensity Score-Based Analysis of Short-Term Complications in Patients with Lumbar Discectomy in the ACS-NSQIP Database Yubo Gao source
pdf		 7
SGF 2012 Propensity Score Analysis and Assessment of Propensity Score Approaches Using SAS Procedures Rheta E. Lanehart,
Patricia Rodríguez de Gil,
Eun Sook Kim,
Aarti P. Bellara,
Jeffrey D. Kromrey,
Reginald Lee		 source 11
SCSUG 2011 When It's Not Random Chance: Creating Propensity Scores Using SAS EG Josie Brunner source 13
WUSS 2011 Causal Methods for Observational Data Amanda Stevenson source 8
SGF 2011 Using SAS Procedures FREQ, GENMOD, LOGISTIC, and PHREG to Estimate Adjusted Relative Risks: A Case Study Jiming Fang source 11
NESUG 2010 An Introduction to Implementing Propensity Score Matching with SAS Kathy Hardis Fraeman 12
WUSS 2010 Get to Know Comparative Effectiveness Research; It's Here To Stay R. Scott Leslie source 7
WUSS 2010 The Last Shall Be First: Matching Patients by Choosing the Least Popular First Stefanie Millar source 9
SESUG 2010 SAS Macros for Estimating the Attributable Benefit of an Optimal Treatment Regime Jason Brinkley source 12
NESUG 2009 Analyzing MCBS Data with PROC SURVEYFREQ: The Maybe Cheesy but Simple Approach Christine E. Leonard 19
WUSS 2008 Automating the process of choosing among highly correlated covariates for multivariable logistic regression Michael C. Doherty source 7
WUSS 2008 Using Propensity Scores To Adjust For Treatment Selection Bias R. Scott Leslie source 7
PharmaSUG 2008 Automating the process of choosing among highly correlated covariates for multivariable logistic regression Michael C. Doherty,
Xiaochun Zhang		 8
PharmaSUG 2008 The Use of Propensity Scores and Instrumental Variable Methods to Adjust For Treatment Selection Bias R. Scott Leslie 8
SGF 2008 Increasing Your Marketing ROI with SAS Marketing Optimization Dwight J. Mouton source 8
SGF 2008 The Use of Propensity Scores and Instrumental Variable Methods to Adjust For Treatment Selection Bias R. Scott Leslie,
Hassan Ghomrawi		 source 8
SESUG 2007 Application of an Exact Covariate Matching Macro in Outcome Analysis Guiping Yang source 19
WUSS 2007 The Use of Propensity Scores and Instrumental Variable Methods to Adjust For Treatment Selection Bias R. Scott Leslie source 8
SCSUG 2007 Propensity Scoring Tutorial by Simulation in SAS Dee H. Wu,
David M. Thompson,
David Bard		 11
PharmaSUG 2007 A Review of Propensity Score Application in Healthcare Outcome and Epidemiology Guiping Yang 24
PharmaSUG 2007 Promotion Response Analysis in the Pharmaceutical Industry - Lessons to be Learned From Clinical Trials Methodologies Alejandro Jaramillo 12
SGF 2007 Introducing a SAS Macro for Doubly Robust Estimation Michele Westreich,
Daniel Davidian,
Marie Wiesen,
Chris Jonsson Funk		 source 8
SGF 2007 Local and Global Optimal Propensity Score Matching Marcelo Coca-Perraillon source 9
SGF 2007 Using Propensity Scores To Adjust For Treatment Selection Bias R. Scott Thiebaud,
Patrick Leslie		 source 4
WUSS 2006 Using Propensity Scores To Adjust For Treatment Selection Bias Scott P. Leslie,
Patrick Thiebaud		 source 4
NESUG 2006 Matching with Propensity Scores to Reduce Bias in Observational Studies Marcelo Coca-Perraillon,
Nan Liu		 10
PharmaSUG 2006 A Method/Macro Based on Propensity Score and Mahalanobis Distance to Reduce Bias in Treatment Comparison in Observational Study Wuwei Wayne Feng,
Yu Jun,
Rong Xu		 11
SESUG 2005 The Missing Link: Data Analysis with Missing Information Venita DePuy source 9
PharmaSUG 2005 Using Procedure-Based ODS Data Components in Statistical Reporting Vincent J. Faber 10
SUGI 2005 Rounding after Multiple Imputation with Non-Binary Categorical Covariates Christopher F. Ake source 11
SUGI 2005 Using Procedure-Based ODS Data Components in Statistical Reporting Vincent J. Faber source 10
WUSS 2004 An Approach to Record-Linkage Using Propensity Score Paul D. Frederick source 12
SUGI 2004 An Approach to Record-Linkage Using Propensity Score Paul D. Frederick source 13
SUGI 2004 Performing a 1:N Case-Control Match on Propensity Score Lori S. Parsons source 12
PharmaSUG 2003 Selecting Balanced Cohorts for Comparison in Observational Research Craig Roberts 4
SESUG 2002 Handling Missing Data with Multiple Imputation Using PROC MI in SAS Hossein N. Yarandi source 5
VIEWS 2001 Closing the Loop - Using SAS to Drive CRM Anton Hirschowitz 13
SEUGI 2001 The SAS Solution for Enterprise Marketing Automation - Opening Session Demo [presentation] Nelle Schantz,
T. Randall Betancourt		 source 43
PharmaSUG 2001 Imputation Techniques Using SAS Software For Incomplete Data In Diabetes Clinical Trials Naum M. Khutoryansky,
Won-Chin Huang		 4
PharmaSUG 2001 Reducing Bias in a Propensity Score Matched-Pair Sample Using Greedy Matching Techniques Lori S. Parsons 6
SUGI 2001 Reducing Bias in a Propensity Score Matched-Pair Sample Using Greedy Matching Techniques Lori S. Parsons source 6
SUGI 2000 Multiple Imputation for Missing Data: Concepts and New Development Yang Yuan source 10
SUGI 2000 Using Propensity Scores to Adjust for Group Differences: Examples Comparing Alternative Surgical Methods David J. Pasta source 7
SUGI 2000 Using SAS Software to Perform a Case-Control Match on Propensity Score in an Observational Study Lori S. Parsons source 6
WUSS 1998 Controlling for Group Differences and Counting Rare Events: Propensity Scores with PROC CATMOD and Poisson Regression with PROC GENMOD Stefanie J. Silva,
Lori Potter		 source 5

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