Estimating the treatment effects of multiple drug combinations on multiple outcomes in hypertension.

Abstract

Hypertension management is complex due to the need for multiple drug combinations and consideration of diverse outcomes. Traditional treatment effect estimation methods struggle to address this complexity, as they typically focus on binary treatments and binary outcomes. To overcome these challenges, we introduce a framework that accommodates multiple drug combinations and multiple outcomes (METO). METO uses multi-treatment encoding to handle drug combinations and sequences, distinguishing between effectiveness and safety outcomes by learning the outcome type during prediction. To mitigate confounding bias, METO employs an inverse probability weighting method for multiple treatments, assigning balance weights based on propensity scores. Evaluated on real-world data, METO achieves significant performance improvements over existing methods, with an average improvement of 6.4% in influence function-based precision of estimating heterogeneous effects. A case study demonstrates METO's ability to identify personalized antihypertensive treatments that optimize efficacy and minimize safety risks, highlighting its potential for improving hypertension treatment strategies.