A Tutorial on Net Benefit Regression for Real-World Cost-Effectiveness Analysis Using Censored Data from Randomized or Observational Studies

Shuai Chen, Heejung Bang and Jeffrey S. Hoch
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Shuai Chen: Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA, USA
Heejung Bang: Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA, USA
Jeffrey S. Hoch: Division of Health Policy and Management, Department of Public Health Sciences, University of California, Davis, Sacramento, CA, USA

Medical Decision Making, 2024, vol. 44, issue 3, 239-251

Abstract: Given the increasing popularity of person-level cost-effectiveness analysis using “real-world†data, there is a clear need to understand and use methods for observational data. When the cost-effectiveness data are subject to censoring, ignoring censoring is especially error prone for heavily censored data. We summarize best practice and provide a hands-on example of applying the net benefit regression framework for cost-effectiveness analysis, which works for both observational and randomized studies with possibly censored data. Many existing methods are special cases within this framework. We provide step-by-step guidance, user-friendly R programs, and examples to illustrate 1) fitting net benefit regressions for possibly censored cost-effectiveness data; 2) implementing doubly robust methods combining net benefit regressions and propensity scores, which may increase the chances to obtain consistent estimates in observational studies; 3) constructing cost-effectiveness acceptability curves; and 4) interpreting the results. The methods in this tutorial are easy to use and lead to more reliable and robust results using typical administrative data, thus providing an attractive option for real-world cost-effectiveness analysis using possibly censored observational data sets. Highlights We illustrate the steps involved in carrying out cost-effectiveness analysis using net benefit regressions with possibly censored demo data by providing step-by-step guidance and code applied to a data set. We demonstrate the importance of these new methods by illustrating how naïve methods for handling censoring can lead to biased cost-effectiveness results.

Keywords: censoring; cost-effectiveness analysis; net benefit regression; non-randomized study; observational data; propensity scores (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:44:y:2024:i:3:p:239-251

DOI: 10.1177/0272989X241230071

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