Predicting counterfactual risks under hypothetical treatment strategies: an application to HIV.

ABSTRACT: The accuracy of a prediction algorithm depends on contextual factors that may vary across deployment settings. To address this inherent limitation of prediction, we propose an approach to counterfactual prediction based on the g-formula to predict risk across populations that differ in their distribution of treatment strategies. We apply this to predict 5-year risk of mortality among persons receiving care for HIV in the U.S. Veterans Health Administration under different hypothetical treatment strategies. First, we implement a conventional approach to develop a prediction algorithm in the observed data and show how the algorithm may fail when transported to new populations with different treatment strategies. Second, we generate counterfactual data under different treatment strategies and

SUBMITTER: Dickerman BA 

PROVIDER: S-EPMC9189026 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature