Understanding differences between what alternate propensity score methods estimate

BACKGROUND: Many approaches to propensity score methods are used in the applied health economics and outcomes research literature. Often this creates confusion when different approaches produce different results for the same data. OBJECTIVE: To present a conceptual overview based on a potential outcomes framework to demonstrate how more than 1 mean treatment effect parameter can be estimated using the propensity score methods and how the selection of appropriate methods should align with the scientific questions. METHODS: We highlight that more than 1 mean treatment effect parameter can be estimated using the propensity score methods. Using the potential outcomes framework and alternate data-generating processes, we discuss under what assumptions different mean treatment effect parameter estimates are supposed to vary. We tie these discussions with propensity score methods to show that different approaches may estimate different parameters. We illustrate these methods using a case study of the comparative effectiveness of apixaban vs warfarin on the likelihood of stroke among patients with a prior diagnosis of atrial fibrillation. RESULTS: Different mean treatment effect parameters take on different values when treatment effects are heterogeneous. We show that traditional propensity score approaches, such as blocking, weighting, matching, or doubly robust, can estimate different mean treatment effect parameters. Therefore, they may not produce the same results even when applied to the same data using the same covariates. We found significant differences in our case study estimates of mean treatment effect parameters. Still, once a mean treatment effect parameter is targeted, estimates across different methods are not different. This highlights the importance of first selecting the target parameter for analysis by aligning the interpretation of the target parameter with the scientific questions and then selecting the specific method to estimate this target parameter. CONCLUSIONS: We present a conceptual overview of propensity score methods in health economics and outcomes research from a potential outcomes framework. We hope these discussions will help applied researchers choose appropriate propensity score approaches for their analysis. DISCLOSURES: Dr Unuigbe's time was supported through an unrestricted postdoctoral fellowship from Pfizer to the University of Washington, Seattle.