A Two-Step Bayesian Approach for Propensity Score Analysis: Simulations and Case Study

Autor/inn/en	Kaplan, David; Chen, Jianshen
Titel	A Two-Step Bayesian Approach for Propensity Score Analysis: Simulations and Case Study
Quelle	In: Psychometrika, 77 (2012) 3, S.581-609 (29 Seiten) PDF als Volltext Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0033-3123
DOI	10.1007/s11336-012-9262-8
Schlagwörter	Intervals; Bayesian Statistics; Scores; Prior Learning; Equations (Mathematics); Simulation; Case Studies; Comparative Analysis; Data Analysis + Suchen Sie Ihr Suchwort? Vorkenntnisse; Equations; Mathematics; Gleichungslehre; Simulation program; Simulationsprogramm; Case study; Fallstudie; Case Study; Auswertung
Abstract	A two-step Bayesian propensity score approach is introduced that incorporates prior information in the propensity score equation and outcome equation without the problems associated with simultaneous Bayesian propensity score approaches. The corresponding variance estimators are also provided. The two-step Bayesian propensity score is provided for three methods of implementation: propensity score stratification, weighting, and optimal full matching. Three simulation studies and one case study are presented to elaborate the proposed two-step Bayesian propensity score approach. Results of the simulation studies reveal that greater precision in the propensity score equation yields better recovery of the frequentist-based treatment effect. A slight advantage is shown for the Bayesian approach in small samples. Results also reveal that greater precision around the wrong treatment effect can lead to seriously distorted results. However, greater precision around the correct treatment effect parameter yields quite good results, with slight improvement seen with greater precision in the propensity score equation. A comparison of coverage rates for the conventional frequentist approach and proposed Bayesian approach is also provided. The case study reveals that credible intervals are wider than frequentist confidence intervals when priors are non-informative. (As Provided).
Anmerkungen	Springer. 233 Spring Street, New York, NY 10013. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-348-4505; e-mail: service-ny@springer.com; Web site: http://www.springerlink.com
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10