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

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 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.

A regression framework for a probabilistic measure of cost-effectiveness

“…where N a is the number of participants receiving treatment A = a and R i (λ) is the pooled rank of the INB for the i th individual. Spieker et al proposed a flexible standardization approach to estimate the NBS in the presence of measured confounders (Spieker et al, 2021). By modelling the joint distribution of observed variables, they are able to apply Monte Carlo methodology to sample INBs from treated and untreated populations which have the same underlying distribution of confounders.…”

“…Because more than half of the cost associated with the treatment of endometrial cancer is accrued in the period after initial treatment (Mariotto, Robin Yabroff, Shao, Feuer, & Brown, 2011), it is important to ensure that only those patients who are likely to benefit from adjuvant therapies receive additional treatment. We illustrate how to determine the cost-effectiveness of adjuvant radiation and chemotherapy using empirically-based synthetic data developed by Spieker et al (Spieker et al, 2021). This dataset was developed based on data from a large observational endometrial cancer database.…”

“…In recent work, Spieker et al (Spieker, Illenberger, Roy, & Mitra, 2021) introduced a novel probabilistic measure of cost-effectiveness, the net benefit separation (NBS). The NBS characterises the stochastic ordering of individual net benefits (INB) between treated and untreated populations.…”

“…As with NMB and ICER, extensions of NBS to settings with informative cost censoring and confounding have been proposed. Spieker et al (Spieker et al, 2021) introduce a semiparametric Monte Carlo standardization procedure to estimate NBS while adjusting for differences in the distribution of confounding variables across treatment arms. Models used within the standardization procedure may be fit using IPCW techniques if there are censored cost data.…”

“…The remainder of this paper is organized as follows: In Section 2 we review the net benefit separation as proposed by Spieker et al (Spieker et al, 2021). In Section 3 we define a conditional variant of the NBS and introduce our regression-based estimation procedure.…”