Propensity score analysis fundamentals and developments
Machine generated contents note: I. Fundamentals of Propensity Score Analysis1. 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 C...
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| Weitere Verfasser: | , |
| Format: | UnknownFormat |
| Sprache: | eng |
| Veröffentlicht: |
New York, London
The Guilford Press
2015
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| Schriftenreihe: | Research methods
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Tag hinzufügen | Zusammenfassung: | Machine generated contents note: I. Fundamentals of Propensity Score Analysis1. 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. "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"-- "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. "-- |
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| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | xiv, 402 Seiten Diagramme 25 cm |
| ISBN: | 9781462519491 978-1-4625-1949-1 |