Propensity score methods and regression adjustment for analysis of nonrandomized studies with health-related quality of life outcomes

Purpose: The aim of this study was to investigate the potential added value of combining propensity score (PS) methods with multivariable linear regression (MLR) in estimating the average treatment effect on the treated (ATT) in nonrandomized studies with health-related quality of life (HRQoL) outcomes. Methods: We first used simulations to compare the performances of different PS-based methods, either alone or in combination with further MLR adjustment, in estimating ATT. PS methods were, respectively, optimal pair (OPM) and full (OFM) PS matching, subclassification on the PS (SBC), and the inverse probability of treatment weighting (IPTW). We simulated several scenarios, according to different sample sizes, proportions of treated vs untreated subjects, and types of HRQoL outcomes. We also applied the same methods to a real clinical data set. Results: OPM and IPTW provided the closest Type I error to the nominal threshold alpha = 0.05 across all scenarios. Overall, both methods showed also lower variability in estimates than SBC and OFM. SBC performed worst, generally providing the highest levels of bias. Further MLR adjustment lessened bias for all methods, however providing higher Type I error for SBC and OFM. In the real case, all methods provided similar ATT estimates except for one outcome. Conclusions: Our findings suggest that for sample sizes up to n = 200, OPM and IPTW are to be preferred to OFM and SBC in estimating ATT on HRQoL outcomes. Specifically, OPM performed best in sample sizes of n >= 80, IPTW for smaller sample sizes. Additional MLR adjustment can further improve ATT estimates.