Estimating the treatment effects of multiple drug combinations on multiple outcomes in hypertension
Year: 2025
Type: article
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. • Deep learning model to estimate treatment effects of drug combinations • Propose multi-treatment encoding for handling multiple combinations and sequences • Achieve superior results on synthetic and real-world data compared to prior methods • Recommend optimal antihypertensive regimens with maximal efficacy and minimal risks Liu et al. develop a deep learning-based model to estimate the treatment effects of multiple drug combinations on both effectiveness and safety outcomes for hypertension. Evaluated on synthetic and real-world data, the model achieves state-of-the-art performance and demonstrates potential for identifying personalized antihypertensive regimens.
Source: Cell Reports Medicine
Authors: Ruoqi Liu, Lang Li, Ping Zhang
Institution: The Ohio State University
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $5,620
OpenAlex ID: W4407369431