Comparing Different Methods of Applying Propensity Score in Controlling Selection Bias for Studies of Prostate Cancer

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Background: Prostate cancer is the most common cancer among men. There is growing interest in population-based comparative effectiveness research on different therapies for prostate cancer because of the limitations on generalizability of randomized controlled trials. The major challenge for population-based studies using observational data is how to control for selection bias. The objective of this study was to compare the performance of four common methods of applying propensity scores—covariate adjustment, stratification, matching, and inverse probability of treatment weighting—to adjust for confounding in analyses of treatment effects among prostate cancer patients. Methods: The linked Surveillance, Epidemiology, and End Results (SEER) Medicare database 1992-2007 was used for this research in which we studied two scenarios. In scenario 1, the overall mortality and cause-specific mortality for patients with local prostate cancer receiving active treatment (radical prostatectomy or radiation) vs. observation were compared. The known confounding factor, comorbidity, was removed in the analyses to evaluate the selection bias for general health. In scenario 2, we compared the overall mortality and cause-specific mortality between patients with and without primary androgen deprivation therapy. The known confounding factor, cancer grade, was removed in the analyses to evaluate the selection bias for cancer severity. Results: Among four propensity methods, matching (greedy matching and caliper matching) produced the estimates that were closest to the estimates when the removed confounder was included in both scenarios. Covariate adjustment and stratification yielded similar controlling effects in both scenarios. Inverse probability of treatment weighting showed a better performance in scenario 1 compared to scenario 2. Conclusions: Propensity score matching outperformed covariates adjustment, stratification, and inverse probability of treatment weighting. None of the propensity methods produced estimates that were identical to the estimates when the removed confounders, comorbidity or cancer grade, were included.

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Propensity score, prostate cancer, observational study
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