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Bayesian sample size determination for cost-effectiveness studies with censored data.

Daniel P Beavers1, James D Stamey2

  • 1Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, NC, United States of America.

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|January 6, 2018
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Summary
This summary is machine-generated.

This study introduces a Bayesian approach for sample size determination in cost-effectiveness studies with censored data. This method addresses limitations of existing models, improving accuracy for survival-based analyses.

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Area of Science:

  • Health economics
  • Biostatistics
  • Clinical trial design

Background:

  • Cost-effectiveness analysis (CEA) is vital for comparing treatments.
  • Existing sample size methods struggle with censored data common in survival studies.
  • This limitation impacts the reliability of economic evaluations in clinical research.

Purpose of the Study:

  • To propose a novel Bayesian method for sample size determination in CEA.
  • To accommodate censored cost and effectiveness data in survival-based studies.
  • To provide accurate sample size approximations for both statistical power and assurance.

Main Methods:

  • Developed a Bayesian framework for designing and analyzing CEA data.
  • Incorporated methods to handle censored costs and effectiveness outcomes.
  • Explored two distinct parametric models to assess approach flexibility.
  • Approximated sample size for both power and assurance.

Main Results:

  • The proposed Bayesian method effectively handles censored data in CEA.
  • The approach demonstrated flexibility with different study assumptions.
  • Accurate sample size determination is achievable even with incomplete data.

Conclusions:

  • The Bayesian method offers a robust solution for sample size calculation in survival-based CEA.
  • This advances the design and analysis of cost-effectiveness studies with censored outcomes.
  • Facilitates more reliable health economic evaluations in clinical practice.