The performance of marginal structural models for estimating risk differences and relative risks using weighted univariate generalized linear models.

ABSTRACT: We used Monte Carlo simulations to compare the performance of marginal structural models (MSMs) based on weighted univariate generalized linear models (GLMs) to estimate risk differences and relative risks for binary outcomes in observational studies. We considered four different sets of weights based on the propensity score: inverse probability of treatment weights with the average treatment effect as the target estimand, weights for estimating the average treatment effect in the treated, matching weights and overlap weights. We considered sample sizes ranging from 500 to 10,000 and allowed the prevalence of treatment to range from 0.1 to 0.9. We examined both the robust variance estimator when using generalized estimating equations with an independent working correlation matrix and a boo

SUBMITTER: Austin PC 

PROVIDER: S-EPMC11162095 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature