A regression framework for a probabilistic measure of cost-effectiveness

TL;DR

This paper introduces a regression framework for the net benefit separation (NBS), a probabilistic measure of cost-effectiveness, enabling covariate-specific analysis and addressing confounding and censoring in health policy decision-making.

Contribution

It develops a novel regression method for NBS that accounts for informative cost censoring and confounding, enhancing analysis of cost-effectiveness factors.

Findings

NBS regression performs well in simulations.

The method can identify determinants of cost-effectiveness.

Application to simulated cancer data illustrates its utility.

Abstract

To make informed health policy decisions regarding a treatment, we must consider both its cost and its clinical effectiveness. In past work, we introduced the net benefit separation (NBS) as a novel measure of cost-effectiveness. The NBS is a probabilistic measure that characterizes the extent to which a treated patient will be more likely to experience benefit as compared to an untreated patient. Due to variation in treatment response across patients, uncovering factors that influence cost-effectiveness can assist policy makers in population-level decisions regarding resource allocation. In this paper, we introduce a regression framework for NBS in order to estimate covariate-specific NBS and find determinants of variation in NBS. Our approach is able to accommodate informative cost censoring through inverse probability weighting techniques, and addresses confounding through a semiparametric standardization procedure. Through simulations, we show that NBS regression performs well in a variety of common scenarios. We apply our proposed regression procedure to a realistic simulated data set as an illustration of how our approach could be used to investigate the association between cancer stage, comorbidities and cost-effectiveness when comparing adjuvant radiation therapy and chemotherapy in post-hysterectomy endometrial cancer patients.