Common Methods for Handling Missing Data in Marginal Structural Models: What Works and Why
Photo: Pixabay / Gerd Altmann We recommend careful consideration of 1) the reasons for missingness, 2) whether missingness modifies the existing relationships among observed data, and 3) the scientific context and data source, to inform the choice of the appropriate method(s) for handling partially observed confounders in MSMs. Read the paper at https://academic.oup.com/aje/article/190/4/663/59238
https://www.lupop.lu.se/article/common-methods-handling-missing-data-marginal-structural-models-what-works-and-why - 2025-09-11