Piecewise propensity score analysis: A new method for conducting propensity score matching with polytomous ordinal independent variables.

SCIENTIFIC Propensity score analysis is widely used for simulating random assignment in observational studies when true random assignment is not possible. In propensity score modeling, a number of covariates are used to estimate the probability that an individual will belong to 1 of 2 groups. Prospective participants are then matched on their probabilities of belonging to the 2 groups rather than on the exact set of covariate values (as in traditional matching methods). However, traditional propensity score analysis can only be used in studies with 2 groups, such as an experimental and a control group. In this article, we propose a new method called piecewise propensity score analysis (PPSA) for ordinal polytomous grouping variables. We compared PPSA with another method of conducting propensity score analysis with ordered categories, marginal mean weighting through stratification (MMW-S; Hong, 2010, 2012) in a 3 × 5 × 4 study across three model misspecification conditions, five matching methods, and four sample sizes (1,000, 5,000, 10,000, 21,753). We found no significant difference between PPSA and MMW-S methods across conditions. We recommend linear regression, simple mean difference, or propensity stratification methods for simulating causal inference. (PsycINFO Database Record (c) 2018 APA, all rights reserved)