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

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Summary

This study provides guidance and code for cost-effectiveness analysis using net benefit regressions with censored data. Naïve censoring methods can lead to biased results, highlighting the importance of these new techniques.

Keywords:
censoringcost-effectiveness analysisnet benefit regressionnon-randomized studyobservational datapropensity scores

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Area of Science:

  • Health Economics
  • Biostatistics

Background:

  • Cost-effectiveness analysis (CEA) is crucial for healthcare decision-making.
  • Censored data is common in economic evaluations, posing analytical challenges.
  • Existing methods for handling censored data in CEA may yield biased results.

Purpose of the Study:

  • To provide a step-by-step guide for conducting CEA using net benefit regressions with censored data.
  • To demonstrate the application of these methods with practical code examples.
  • To highlight the potential biases arising from naive approaches to censoring.

Main Methods:

  • Utilizing net benefit regressions for cost-effectiveness analysis.
  • Implementing methods to appropriately handle censored data.
  • Applying guidance to a real-world demo dataset.

Main Results:

  • Demonstrated a clear methodology for CEA with censored data.
  • Illustrated how naive censoring methods can produce biased cost-effectiveness estimates.
  • Provided reproducible code for practical application.

Conclusions:

  • Net benefit regressions offer a robust approach for CEA with censored data.
  • Correctly handling censored data is essential to avoid biased economic evaluations.
  • The provided guidance and code facilitate accurate and reliable CEA.