1st Edition
Propensity Score Analysis Fundamentals and Developments
402 Pages
by
Guilford Press
This book is designed to help researchers better design and analyze observational data from quasi-experimental studies and improve the validity of research on causal claims. It provides clear guidance on the use of different propensity score analysis (PSA) methods, from the fundamentals to complex, cutting-edge techniques. Experts in the field introduce underlying concepts and current issues and review relevant software programs for PSA. The book addresses the steps in propensity score estimation, including the use of generalized boosted models, how to identify which matching methods work best with specific types of data, and the evaluation of balance results on key background covariates after matching. Also covered are applications of PSA with complex data, working with missing data, controlling for unobserved confounding, and the extension of PSA to prognostic score analysis for causal inference. User-friendly features include statistical program codes and application examples. Data and software code for the examples are available at the companion website (www.guilford.com/pan-materials).
I. Fundamentals of Propensity Score Analysis
1. Propensity Score Analysis: Concepts and Issues, Wei Pan & Haiyan Bai
2. Overview of Implementing Propensity Score Analysis in Statistical Software, Megan Schuler
II. Propensity Score Estimation, Matching, and Covariate Balance
3. Propensity Score Estimation with Boosted Regression, Lane F. Burgette, Daniel F. McCaffrey, & Beth Ann Griffin
4. Methodological Considerations in Implementing Propensity Score Matching, Haiyan Bai
5. Evaluating Covariate Balance, Cassandra W. Pattanayak
III. Weighting Schemes and Other Strategies for Outcome Analysis after Matching
6. Propensity Score Adjustment Methods, M. H. Clark
7. Propensity Score Analysis with Matching Weights, Liang Li, Tom H. Greene, & Brian C. Sauer
8. Robust Outcome Analysis for Propensity-Matched Designs, Scott F. Kosten, Joseph W. McKean, & Bradley E. Huitema
IV. Propensity Score Analysis on Complex Data
9. Latent Growth Modeling of Longitudinal Data with Propensity-Score-Matched Groups, Walter L. Leite
10. Propensity Score Matching on Multilevel Data, Qiu Wang
11. Propensity Score Analysis with Complex Survey Samples, Debbie L. Hahs-Vaughn
V. Sensitivity Analysis and Extensions Related to Propensity Score Analysis
12. Missing Data in Propensity Scores, Robin Mitra
13. Unobserved Confounding in Propensity Score Analysis, Rolf H. H. Groenwold & Olaf H. Klungel
14. Propensity-Score-Based Sensitivity Analysis, Lingling Li, Changyu Shen, & Xiaochun Li
15. Prognostic Scores in Clustered Settings, Ben Kelcey & Christopher M. Swoboda
Author Index
Subject Index
About the Editors
Contributors
1. Propensity Score Analysis: Concepts and Issues, Wei Pan & Haiyan Bai
2. Overview of Implementing Propensity Score Analysis in Statistical Software, Megan Schuler
II. Propensity Score Estimation, Matching, and Covariate Balance
3. Propensity Score Estimation with Boosted Regression, Lane F. Burgette, Daniel F. McCaffrey, & Beth Ann Griffin
4. Methodological Considerations in Implementing Propensity Score Matching, Haiyan Bai
5. Evaluating Covariate Balance, Cassandra W. Pattanayak
III. Weighting Schemes and Other Strategies for Outcome Analysis after Matching
6. Propensity Score Adjustment Methods, M. H. Clark
7. Propensity Score Analysis with Matching Weights, Liang Li, Tom H. Greene, & Brian C. Sauer
8. Robust Outcome Analysis for Propensity-Matched Designs, Scott F. Kosten, Joseph W. McKean, & Bradley E. Huitema
IV. Propensity Score Analysis on Complex Data
9. Latent Growth Modeling of Longitudinal Data with Propensity-Score-Matched Groups, Walter L. Leite
10. Propensity Score Matching on Multilevel Data, Qiu Wang
11. Propensity Score Analysis with Complex Survey Samples, Debbie L. Hahs-Vaughn
V. Sensitivity Analysis and Extensions Related to Propensity Score Analysis
12. Missing Data in Propensity Scores, Robin Mitra
13. Unobserved Confounding in Propensity Score Analysis, Rolf H. H. Groenwold & Olaf H. Klungel
14. Propensity-Score-Based Sensitivity Analysis, Lingling Li, Changyu Shen, & Xiaochun Li
15. Prognostic Scores in Clustered Settings, Ben Kelcey & Christopher M. Swoboda
Author Index
Subject Index
About the Editors
Contributors
Biography
Wei Pan, PhD, is Associate Professor and Biostatistician in the School of Nursing at Duke University. His research interests include causal inference (confounding, propensity score analysis, and resampling), advanced modeling (multilevel, structural, and mediation and moderation), meta-analysis, and their applications in the social, behavioral, and health sciences. Dr. Pan has published over 50 articles in refereed journals, as well as other publications, and has served on the editorial boards of several journals.He is the recipient of several awards for excellence in research, teaching, and service.
Haiyan Bai, PhD, is Associate Professor of Quantitative Research Methodology at the University of Central Florida. Her interests include resampling methods, propensity score analysis, research design, measurement and evaluation, and the applications of statistical methods in the educational and behavioral sciences. She has published a book on resampling methods as well as numerous articles in refereed journals, and has served on the editorial boards of several journals. Dr. Bai is a Fellow of the Academy for Teaching, Learning, and Leadership and a Faculty Fellow at the University of Central Florida, where she has been the recipient of several awards for excellence in research and teaching.
Haiyan Bai, PhD, is Associate Professor of Quantitative Research Methodology at the University of Central Florida. Her interests include resampling methods, propensity score analysis, research design, measurement and evaluation, and the applications of statistical methods in the educational and behavioral sciences. She has published a book on resampling methods as well as numerous articles in refereed journals, and has served on the editorial boards of several journals. Dr. Bai is a Fellow of the Academy for Teaching, Learning, and Leadership and a Faculty Fellow at the University of Central Florida, where she has been the recipient of several awards for excellence in research and teaching.
"Pan and Bai have assembled a comprehensive volume on all aspects of propensity score methods. Both the user and the statistician will find something to like in this book. I recommend it."--William R. Shadish, PhD, Distinguished Professor of Psychology, University of California, Merced-
"This book effectively synthesizes general principles of PSA with recent developments regarding complex issues such as estimation techniques, covariate balance, weighting, complex datasets, and sensitivity analysis. The discussion of statistical software and examples of computer code are helpful additions. This book will be useful to graduate students and applied researchers who are interested in learning about PSA for the first time or who have some knowledge and would like to learn about issues and recent developments. I recommend it as a textbook for graduate-level courses in methods of causal inference or as a reference for researchers in the social and biomedical sciences."--Suzanne E. Graham, EdD, Department of Education, University of New Hampshire
"There is no question that this book will serve as an excellent resource for those who want to add PSA to their repertoire of analytical methods. The chapters provide sufficient materials and examples to help both newbies and seasoned analysts deal with the methodological and practical challenges of applying PSA in research work."--Xitao Fan, PhD, Chair Professor and Dean, Faculty of Education, University of Macau, China
"This book is a go-to guide for designing and analyzing observational data. The editors have produced a brilliant work that addresses both methodological and practical issues in propensity score analysis. A 'must read' for all biostatisticians as well as applied researchers in the social, behavioral, and health sciences."--Ding-Geng (Din) Chen, PhD, School of Nursing and Department of Biostatistics and Computational Biology, University of Rochester Medical Center
