Marginal Structural Models as a Tool for Standardization

TLDR

Standardization controls confounding but suffers from sparse‑data problems when many confounders are stratified, prompting the development of marginal structural models that use inverse‑probability weighting for nonparametric causal estimation. The article demonstrates the general relationship between standardization techniques and marginal structural models. The authors modify marginal structural model weights to enable nonparametric estimation of standardized parameters. Using the modified weights, marginal structural models become a useful tool for nonparametric multivariate standardization.

Abstract

In this article, we show the general relation between standardization methods and marginal structural models. Standardization has been recognized as a method to control confounding and to estimate causal parameters of interest. Because standardization requires stratification by confounders, the sparse-data problem will occur when stratified by many confounders and one then might have an unstable estimator. A new class of causal models called marginal structural models has recently been proposed. In marginal structural models, the parameters are consistently estimated by the inverse-probability-of-treatment weighting method. Marginal structural models give a nonparametric standardization using the total group (exposed and unexposed) as the standard. In epidemiologic analysis, it is also important to know the change in the average risk of the exposed (or the unexposed) subgroup produced by exposure, which corresponds to the exposed (or the unexposed) group as the standard. We propose modifications of the weights in the marginal structural models, which give the nonparametric estimation of standardized parameters. With the proposed weights, we can use the marginal structural models as a useful tool for the nonparametric multivariate standardization.