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

Publié par Clémence Leyrat, en 2020 - Am Journal Epidemiol

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.

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