Common Methods for Handling Missing Data in Marginal Structural Models: What Works and Why.

ABSTRACT: Marginal structural models (MSMs) are commonly used to estimate causal intervention effects in longitudinal nonrandomized studies. A common challenge when using MSMs to analyze observational studies is incomplete confounder data, where a poorly informed analysis method will lead to biased estimates of intervention effects. Despite a number of approaches described in the literature for handling missing data in MSMs, there is little guidance on what works in practice and why. We reviewed existing missing-data methods for MSMs and discussed the plausibility of their underlying assumptions. We also performed realistic simulations to quantify the bias of 5 methods used in practice: complete-case analysis, last observation carried forward, the missingness pattern approach, multiple imputation, a

SUBMITTER: Leyrat C 

PROVIDER: S-EPMC8631064 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature