Learning Interactions Between Continuous Treatments and Covariates with a Semiparametric Model

TL;DR

This paper introduces a semiparametric model for estimating the effects of continuous treatments on binary outcomes, combining interpretability with flexibility, and demonstrates its effectiveness through simulations and a real-world pharmacogenomics case study.

Contribution

It proposes a novel semiparametric framework that decomposes treatment effects into prognostic and interaction scores, improving modeling of continuous treatments without strong linearity assumptions.

Findings

The model converges empirically in simulations.

It provides personalized warfarin dosing recommendations.

The approach enhances interpretability and flexibility in treatment effect estimation.

Abstract

Estimating the impact of continuous treatment variables (e.g., dosage amount) on binary outcomes presents significant challenges in modeling and estimation because many existing approaches make strong assumptions that do not hold for certain continuous treatment variables. For instance, traditional logistic regression makes strong linearity assumptions that do not hold for continuous treatment variables like time of initiation. In this work, we propose a semiparametric regression framework that decomposes effects into two interpretable components: a prognostic score that captures baseline outcome risk based on a combination of clinical, genetic, and sociodemographic features, and a treatment-interaction score that flexibly models the optimal treatment level via a nonparametric link function. By connecting these two parametric scores with Nadaraya-Watson regression, our approach is both interpretable and flexible. The potential of our approach is demonstrated through numerical simulations that show empirical estimation convergence. We conclude by applying our approach to a real-world case study using the International Warfarin Pharmacogenomics Consortium (IWPC) dataset to show our approach's clinical utility by deriving personalized warfarin dosing recommendations that integrate both genetic and clinical data, providing insights towards enhancing patient safety and therapeutic efficacy in anticoagulation therapy.