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Bayesian Solutions for Handling Uncertainty in Survival Extrapolation.

Miguel A Negrín1,2, Julian Nam1, Andrew H Briggs1

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Bayesian model averaging (BMA) better addresses uncertainty in survival models than single best-fit approaches. This improves cost-effectiveness analysis for medical devices like hip prostheses.

Keywords:
Bayesian statistical methodsMarkov modelsacceptability curvescost-effectiveness analysisdecision analysiseconometric methodsprovider decision makingsurvival analysis

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Area of Science:

  • Health economics
  • Biostatistics
  • Medical device analysis

Background:

  • Single best-fit models for survival extrapolation can be unreliable due to model and parameter uncertainty.
  • Bayesian model averaging (BMA) offers a method to incorporate both types of uncertainty.

Purpose of the Study:

  • To investigate the utility of BMA in accounting for model uncertainty in survival analysis.
  • To compare the cost-effectiveness of hip prostheses using BMA versus traditional methods.

Main Methods:

  • Applied BMA to registry data comparing Charnley and Spectron hip prostheses.
  • Incorporated optimistic and skeptical distributions to address parameter stability uncertainty.
  • Averaged distributions using posterior probabilities and compared cost-effectiveness over 8 and 16 years.

Main Results:

  • While initial revision-free years were similar, significant variability emerged over the decision horizon.
  • BMA indicated Spectron was cost-effective with 93% probability at £20,000, contrasting with the best-fit model's 100% and later 0% probability.
  • Considerable uncertainty in the Spectron shape parameter was detected.

Conclusions:

  • Single best-fit models may inadequately capture model uncertainty in survival analysis.
  • BMA, weighted by posterior probabilities, provides a more robust approach to addressing model uncertainty.
  • Regularly updating health economic models remains crucial, even with low initial decision uncertainty.