Reflection on modern methods: combining weights for confounding and missing data.

ABSTRACT: Inverse probability weights are increasingly used in epidemiological analysis, and estimation and application of weights to address a single bias are well discussed in the literature. Weights to address multiple biases simultaneously (i.e. a combination of weights) have almost exclusively been discussed related to marginal structural models in longitudinal settings where treatment weights (estimated first) are combined with censoring weights (estimated second). In this work, we examine two examples of combined weights for confounding and missingness in a time-fixed setting in which outcome or confounder data are missing, and the estimand is the marginal expectation of the outcome under a time-fixed treatment. We discuss the identification conditions, construction of combined weights and ho

SUBMITTER: Ross RK 

PROVIDER: S-EPMC9082798 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature