Estimation and Validation of Ratio-based Conditional Average Treatment Effects Using Observational Data.

ABSTRACT: While sample sizes in randomized clinical trials are large enough to estimate the average treatment effect well, they are often insufficient for estimation of treatment-covariate interactions critical to studying data-driven precision medicine. Observational data from real world practice may play an important role in alleviating this problem. One common approach in trials is to predict the outcome of interest with separate regression models in each treatment arm, and estimate the treatment effect based on the contrast of the predictions. Unfortunately, this simple approach may induce spurious treatment-covariate interaction in observational studies when the regression model is misspecified. Motivated by the need of modeling the number of relapses in multiple sclerosis patients, where the r

SUBMITTER: Yadlowsky S 

PROVIDER: S-EPMC7985957 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature