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.

• I. Fundamentals of Propensity Score Analysis1. Propensity Score Analysis: Concepts and Issues, Wei Pan & Haiyan Bai2. Overview of Implementing Propensity Score Analysis in Statistical Software, Megan SchulerII. Propensity Score Estimation, Matching, and Covariate Balance3. Propensity Score Estimation with Boosted Regression, Lane F. Burgette, Daniel F. McCaffrey, & Beth Ann Griffin4. Methodological Considerations in Implementing Propensity Score Matching, Haiyan Bai5. Evaluating Covariate Balance, Cassandra W. PattanayakIII. Weighting Schemes and Other Strategies for Outcome Analysis after Matching6. Propensity Score Adjustment Methods, M. H. Clark7. Propensity Score Analysis with Matching Weights, Liang Li, Tom H. Greene, & Brian C. Sauer8. Robust Outcome Analysis for Propensity-Matched Designs, Scott F. Kosten, Joseph W. McKean, & Bradley E. HuitemaIV. Propensity Score Analysis on Complex Data9. Latent Growth Modeling of Longitudinal Data with Propensity-Score-Matched Groups, Walter L. Leite10. Propensity Score Matching on Multilevel Data, Qiu Wang11. Propensity Score Analysis with Complex Survey Samples, Debbie L. Hahs-VaughnV. Sensitivity Analysis and Extensions Related to Propensity Score Analysis12. Missing Data in Propensity Scores, Robin Mitra13. Unobserved Confounding in Propensity Score Analysis, Rolf H. H. Groenwold & Olaf H. Klungel14. Propensity-Score-Based Sensitivity Analysis, Lingling Li, Changyu Shen, & Xiaochun Li15. Prognostic Scores in Clustered Settings, Ben Kelcey & Christopher M. SwobodaAuthor IndexSubject IndexAbout the EditorsContributors

"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-