Background: Propensity score (PS) is a popular method for reducing multiple confounding effects in observational studies. It is applicable mainly for situations wherein the exposure/treatment of interest is dichotomous and the PS can be estimated through logistic regression. However, multinomial exposures with 3 or more levels are not rare, e.g., when considering genetic variants, such as single nucleotide polymorphisms (SNPs), which have 3 levels (aa/aA/AA), as an exposure. Conventional PS is inapplicable for this situation unless the 3 levels are collapsed into 2 classes first.
Methods: A simulation study was conducted to compare the performance of the proposed multinomial propensity score (MPS) method under various contrast codings and approaches, including regression adjustment and matching.
Results: MPS methods had more reasonable type I error rate than the non-MPS methods, of which the latter could be as high as 30~50%. Compared with MPS-direct adjusted methods, MPS-matched cohort methods have better power but larger type I error rate. Performance of contrast codings depend on the selection of MPS models.
Conclusions: In general, two combinations had relatively better performance in our simulation of ternary exposure: MPS-matched cohort method with Helmert contrast and MPS-direct adjusted regression with treatment contrasts. Compared with the latter, the former had better power but larger type I error rate as a trade-off.