A Bayesian framework for cost-effectiveness analysis with time-varying treatment decisions

2026-01-19

AI Summary

This study addresses time-varying confounding bias in observational healthcare data—arising from non-random treatment assignment, administrative censoring, and irregular follow-up, exemplified by heterogeneous timing of adjuvant radiotherapy initiation among high-risk early-stage endometrial cancer patients—by proposing the first continuous-time Bayesian framework that jointly models medical costs and time-to-event outcomes to support dynamic treatment strategies. The approach employs Bayesian g-computation to estimate cost-effectiveness measures with causal interpretation and enables posterior comparisons across treatment regimens. By operating in continuous time, it circumvents the data inflation and zero-inflation issues inherent in discrete-time models, while accommodating censoring and dynamic decision-making under minimal parametric assumptions. Simulations demonstrate superior performance over existing methods under various censoring scenarios, and application to SEER-Medicare data yields a robust evaluation of the cost-effectiveness of initiating adjuvant radiotherapy within six months post-surgery.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Bayesian Learning
• Game Theory and Economic Paradigms: Adversarial Learning

Application Category

• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Cost-effectiveness analyses (CEAs) compare the costs and health outcomes of treatment regimes to inform medical decisions. With observational claims data, CEAs must address nonrandom treatment assignment, administrative censoring, and irregularly spaced medical visits that reflect the continuous timing of care and treatment initiation. In high-risk, early-stage endometrial cancer (HR-EC), adjuvant radiation is initiated at patient-specific times following hysterectomy, causing confounding between treatment and outcomes that can evolve with post-surgical recovery and clinical course. Most existing CEA methods use point-treatment or discrete-time models. However, point-treatment approaches break down with time-varying confounding, while discrete-time models bin continuous time, expand the data into a person-period format, and can induce zero-inflation by creating many intervals with no cost-accruing events. We propose a Bayesian framework for CEAs with sequential decision-making that jointly models costs and event times in continuous time, accounts for administrative censoring, and supports dynamic treatment regimes with minimal parametric assumptions. We use Bayesian g-computation to estimate causally interpretable cost-effectiveness measures, including net monetary benefit, and to compare regimes through posterior contrasts. We evaluate the finite-sample performance of the proposed method in simulations across censoring levels and compare it against discrete-time and fully parametric alternatives. We then use SEER-Medicare data to assess the cost-effectiveness of initiating adjuvant radiation therapy within six months following hysterectomy among HR-EC patients.

Problem

• cost-effectiveness analysis
• time-varying treatment
• observational data
• administrative censoring
• confounding

Innovation

• Bayesian g-computation
• continuous-time modeling
• dynamic treatment regimes
• cost-effectiveness analysis
• time-varying confounding