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Risk Factor Adjustment in Marginal Structural Model Estimation of Optimal Treatment Regimes
Author(s) -
Moodie Erica E. M.
Publication year - 2009
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.200800182
Subject(s) - marginal structural model , confounding , weighting , econometrics , context (archaeology) , inverse probability weighting , marginal model , statistics , propensity score matching , model selection , selection (genetic algorithm) , marginal distribution , outcome (game theory) , computer science , mathematics , regression analysis , medicine , machine learning , random variable , paleontology , mathematical economics , biology , radiology
Marginal structural models (MSMs) are an increasingly popular tool, particularly in epidemiological applications, to handle the problem of time‐varying confounding by intermediate variables when studying the effect of sequences of exposures. Considerable attention has been devoted to the optimal choice of treatment model for propensity score‐based methods and, more recently, to variable selection in the treatment model for inverse weighting in MSMs. However, little attention has been paid to the modeling of the outcome of interest, particularly with respect to the best use of purely predictive, non‐confounding variables in MSMs. Four modeling approaches are investigated in the context of both static treatment sequences and optimal dynamic treatment rules with the goal of estimating a marginal effect with the least error, both in terms of bias and variability.