Net benefit separation and the determination curve: a probabilistic framework for cost-effectiveness estimation

TL;DR

This paper introduces a new probabilistic framework for assessing cost-effectiveness of treatments, accounting for data complexities like confounding and censoring, and extends existing methods such as net benefit and cost-effectiveness acceptability curves.

Contribution

It proposes a novel measure based on stochastic ordering of net benefit distributions, enhancing analysis of cost-effectiveness beyond mean differences.

Findings

The method performs well in simulations with confounding and censoring.

It provides additional insights compared to traditional net monetary benefit.

Application to endometrial cancer data demonstrates practical utility.

Abstract

Considerations regarding clinical effectiveness and cost are essential in comparing the overall value of two treatments. There has been growing interest in methodology to integrate cost and effectiveness measures in order to inform policy and promote adequate resource allocation. The net monetary benefit aggregates information on differences in mean cost and clinical outcomes; the cost-effectiveness acceptability curve was then developed to characterize the extent to which the strength of evidence regarding net monetary benefit changes with fluctuations in the willingness-to-pay threshold. Methods to derive insights from characteristics of the cost/clinical outcomes besides mean differences remain undeveloped but may also be informative. We propose a novel probabilistic measure of cost-effectiveness based on the stochastic ordering of the individual net benefit distribution under each treatment. Our approach is able to accommodate features frequently encountered in observational data including confounding and censoring, and complements the net monetary benefit in the insights it provides. We conduct a range of simulations to evaluate finite-sample performance and illustrate our proposed approach using simulated data based on a study of endometrial cancer patients.