9781462519491 (hardback)

1462519490 (hardback)

"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"-- Provided by publisher.

"Subject Areas/Keywords: advanced quantitative methods, causal analysis, causal inferences, estimation, matching, observational data, propensity score analysis, propensity scores, PSA, quasi-experimental research, research methods, statistics DESCRIPTION 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. "-- Provided by publisher.

I. Fundamentals of Propensity Score Analysis. 1. Propensity Score Analysis: Concepts and Issues / Wei Pan and 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, and 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 and Brian C. Sauer ; 8. Robust Outcome Analysis for Propensity-Matched Designs / Scott F. Kosten, Joseph W. McKean and 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 and Olaf H. Klungel ; 14. Propensity-Score-Based Sensitivity Analysis / Lingling Li, Changyu Shen, and Xiaochun Li ; 15. Prognostic Scores in Clustered Settings / Ben Kelcey and Christopher M. Swoboda

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Subject Index

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