Net-benefit regression with censored cost-effectiveness data from randomized or observational studies

Cost-effectiveness analysis is an essential part of the evaluation of new medical interventions. While in many studies both costs and effectiveness (eg, survival time) are censored, standard survival analysis techniques are often invalid due to the induced dependent censoring problem. We propose methods for censored cost-effectiveness data using the net-benefit regression framework, which allow covariate-adjustment and subgroup identification when comparing two intervention groups. The methods provide a straightforward way to construct cost-effectiveness acceptability curves with censored data. We also propose a more efficient doubly robust estimator of average causal incremental net benefit, which increases the likelihood that the results will represent a valid inference in observational studies. Lastly, we conduct extensive numerical studies to examine the finite-sample performance of the proposed methods, and illustrate the proposed methods with a real data example using both survival time and quality-adjusted survival time as the measures of effectiveness.

Automated summary

This study focuses on methods for censored cost-effectiveness data in the context of new medical interventions evaluation. The proposed methods include the net-benefit regression framework and a doubly robust estimator for average causal incremental net benefit, showing valid inference in observational studies. Extensive numerical studies confirm the finite-sample performance of these methods and demonstrate their application with real data examples.