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

TL;DR

This paper introduces a Bayesian continuous-time framework for cost-effectiveness analysis of dynamic treatment regimes, effectively handling time-varying confounding, censoring, and irregular visit patterns in observational health data.

Contribution

It develops a novel Bayesian approach that models costs and event times jointly, enabling causal inference of treatment strategies with minimal parametric assumptions.

Findings

The method performs well across different censoring levels in simulations.

Application to SEER-Medicare data shows the cost-effectiveness of early radiation therapy.

Compared to existing models, it provides more accurate and interpretable estimates.

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.