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

• Published in: Statistics in medicine Vol. 41; no. 20; pp. 3958 - 3974
• Main Authors: Chen, Shuai, Hoch, Jeffrey S.
• Format: Journal Article
• Language: English
• Published: England Wiley Subscription Services, Inc 10.09.2022
• Subjects: censored data; cost‐effectiveness analysis; double robustness; inverse‐probability weighting; Medical research; Medical statistics; net‐benefit regression; Observational studies; Statistical analysis; net-benefit regression; censored data; double robustness; cost-effectiveness analysis; inverse-probability weighting
• ISSN: 0277-6715; 1097-0258; 1097-0258
• DOI: 10.1002/sim.9486

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.