Performance evaluation of propensity score methods for estimating average treatment effects with multi-level treatments

Hui Nian, Chang Yu, Juan Ding, Huiyun Wu, William D. Dupont, Steve Brunwasser, Tebeb Gebretsadik, Tina V. Hartert and Pingsheng Wu

Journal of Applied Statistics, 2019, vol. 46, issue 5, 853-873

Abstract: The propensity score (PS) method is widely used to estimate the average treatment effect (TE) in observational studies. However, it is generally confined to the binary treatment assignment. In an extension to the settings of a multi-level treatment, Imbens proposed a generalized propensity score which is the conditional probability of receiving a particular level of the treatment given pre-treatment variables. The average TE can then be estimated by conditioning solely on the generalized PS under the assumption of weak unconfoundedness. In the present work, we adopted this approach and conducted extensive simulations to evaluate the performance of several methods using the generalized PS, including subclassification, matching, inverse probability of treatment weighting (IPTW), and covariate adjustment. Compared with other methods, IPTW had the preferred overall performance. We then applied these methods to a retrospective cohort study of 228,876 pregnant women. The impact of the exposure to different types of the antidepressant medications (no exposure, selective serotonin reuptake inhibitor (SSRI) only, non-SSRI only, and both) during pregnancy on several important infant outcomes (birth weight, gestation age, preterm labor, and respiratory distress) were assessed.

Date: 2019
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DOI: 10.1080/02664763.2018.1523375

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