Comparison of Type I error rates and statistical power of different propensity score methods. Issue 4 (4th March 2018)

• Record Type: Journal Article
• Authors: Turley, Falynn C.; Redden, David; Case, Janice L.; Katholi, Charles; Szychowski, Jeff; DuBay, Derek
• Is Part Of: Journal of statistical computation and simulation. Volume 88:Issue 4(2018)
• Page Start: 769
• Page End: 784
• Publication Date: 2018-03-04
• Subjects: Propensity score -- confounding -- Type I error -- power -- logistic regression; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2017.1406937
• Languages: English
• ISSNs: 0094-9655

Abstract

ABSTRACT: Propensity score analysis (PSA) is a technique to correct for potential confounding in observational studies. Covariate adjustment, matching, stratification, and inverse weighting are the four most commonly used methods involving propensity scores. The main goal of this research is to determine which PSA method performs the best in terms of protecting against spurious association detection, as measured by Type I error rate, while maintaining sufficient power to detect a true association, if one exists. An examination of these PSA methods along with ordinary least squares regression was conducted under two cases: correct PSA model specification and incorrect PSA model specification. PSA covariate adjustment and PSA matching maintain the nominal Type I error rate, when the PSA model is correctly specified, but only PSA covariate adjustment achieves adequate power levels. Other methods produced conservative Type I Errors in some scenarios, while liberal Type I error rates were observed in other scenarios.