Evaluating Flexible Modeling of Continuous Covariates in Inverse-Weighted Estimators.

ABSTRACT: Correct specification of the exposure model is essential for unbiased estimation in marginal structural models with inverse-probability-of-treatment weights. However, although flexible modeling is commonplace when estimating effects of continuous covariates in outcome models, its use is less frequent in estimation of inverse probability weights. Using simulations, we assess the accuracy of the treatment effect estimates and covariate balance obtained with different exposure model specifications when the true relationship between a continuous, possibly time-varying covariate Lt and the logit of the probability of exposure is nonlinear. Specifically, we compare 4 approaches to modeling the effect of Lt when estimating inverse probability weights: a linear function, the covariate-balancing pr

SUBMITTER: Kyle RP 

PROVIDER: S-EPMC6545287 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature