Propensity score analysis :: fundamentals and developments /
"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 com...
Gespeichert in:
| Weitere Verfasser: | |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
New York :
The Guilford Press,
2015.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | "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"-- |
| Beschreibung: | 1 online resource |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 9781462519538 1462519539 |
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| 505 | 0 | |a part II: 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 -- part 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 -- part 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 -- part 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 -- part 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. | |
| 520 | |a "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"-- |c Provided by publisher | ||
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| contents | part II: 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 -- part 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 -- part 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 -- part 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 -- part 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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| indexdate | 2024-11-27T13:26:31Z |
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| spelling | Propensity score analysis : fundamentals and developments / edited by Wei Pan, Haiyan Bai. New York : The Guilford Press, 2015. 1 online resource text txt rdacontent computer c rdamedia online resource cr rdacarrier part II: 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 -- part 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 -- part 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 -- part 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 -- part 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. "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 Includes bibliographical references and index. Print version record. Social sciences Statistical methods. http://id.loc.gov/authorities/subjects/sh85124018 Sciences sociales Méthodes statistiques. PSYCHOLOGY Statistics. bisacsh MEDICAL Research. bisacsh EDUCATION Statistics. bisacsh SOCIAL SCIENCE Statistics. bisacsh REFERENCE Questions & Answers. bisacsh Sciences sociales. eclas Méthodes statistiques. eclas Social sciences Statistical methods fast Pan, Wei, 1958- https://id.oclc.org/worldcat/entity/E39PCjyh3vMgWG3GYrkKTBrcyd has work: Propensity score analysis (Text) https://id.oclc.org/worldcat/entity/E39PCG7YjKpF9vXhBbQ8WbXkym https://id.oclc.org/worldcat/ontology/hasWork Print version : Propensity score analysis. New York : The Guilford Press, [2015] 9781462519491 (DLC) 2015000367 (OCoLC)884303322 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=916659 Volltext |
| spellingShingle | Propensity score analysis : fundamentals and developments / part II: 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 -- part 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 -- part 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 -- part 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 -- part 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. Social sciences Statistical methods. http://id.loc.gov/authorities/subjects/sh85124018 Sciences sociales Méthodes statistiques. PSYCHOLOGY Statistics. bisacsh MEDICAL Research. bisacsh EDUCATION Statistics. bisacsh SOCIAL SCIENCE Statistics. bisacsh REFERENCE Questions & Answers. bisacsh Sciences sociales. eclas Méthodes statistiques. eclas Social sciences Statistical methods fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85124018 |
| title | Propensity score analysis : fundamentals and developments / |
| title_auth | Propensity score analysis : fundamentals and developments / |
| title_exact_search | Propensity score analysis : fundamentals and developments / |
| title_full | Propensity score analysis : fundamentals and developments / edited by Wei Pan, Haiyan Bai. |
| title_fullStr | Propensity score analysis : fundamentals and developments / edited by Wei Pan, Haiyan Bai. |
| title_full_unstemmed | Propensity score analysis : fundamentals and developments / edited by Wei Pan, Haiyan Bai. |
| title_short | Propensity score analysis : |
| title_sort | propensity score analysis fundamentals and developments |
| title_sub | fundamentals and developments / |
| topic | Social sciences Statistical methods. http://id.loc.gov/authorities/subjects/sh85124018 Sciences sociales Méthodes statistiques. PSYCHOLOGY Statistics. bisacsh MEDICAL Research. bisacsh EDUCATION Statistics. bisacsh SOCIAL SCIENCE Statistics. bisacsh REFERENCE Questions & Answers. bisacsh Sciences sociales. eclas Méthodes statistiques. eclas Social sciences Statistical methods fast |
| topic_facet | Social sciences Statistical methods. Sciences sociales Méthodes statistiques. PSYCHOLOGY Statistics. MEDICAL Research. EDUCATION Statistics. SOCIAL SCIENCE Statistics. REFERENCE Questions & Answers. Sciences sociales. Méthodes statistiques. Social sciences Statistical methods |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=916659 |
| work_keys_str_mv | AT panwei propensityscoreanalysisfundamentalsanddevelopments |