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A full Bayesian model to handle structural ones and missingness in economic evaluations from individual-level data.

Andrea Gabrio1, Alexina J Mason2, Gianluca Baio1

  • 1Department of Statistical Science, University College London, London, UK.

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|December 20, 2018
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This study introduces a flexible Bayesian framework for economic evaluations, addressing complex data like nonnormality and missingness. This approach improves the accuracy of resource allocation decisions in healthcare technology appraisal.

Keywords:
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Area of Science:

  • Health economics
  • Biostatistics
  • Clinical trial analysis

Background:

  • Economic evaluations using individual-level data are crucial for healthcare resource allocation.
  • Standardized methods often fail to address complexities in effectiveness and cost data, leading to biased inferences.
  • Data complexities include nonnormality, spikes, and missing values.

Purpose of the Study:

  • To present a general Bayesian framework capable of handling complex data in economic evaluations.
  • To demonstrate the benefits and flexibility of this Bayesian approach using real-world trial data.
  • To highlight the importance of comprehensive modeling in economic evaluations.

Main Methods:

  • Development of a general Bayesian framework for economic evaluations.
  • Application and comparison of increasingly complex models to real trial data (MenSS and PBS trials).
  • Sensitivity analysis to assess the robustness of findings to missingness assumptions.

Main Results:

  • The Bayesian framework effectively handles data complexities such as spikes and missingness.
  • The MenSS trial example demonstrated benefits with spikes in effectiveness and missing outcomes.
  • The PBS trial example showcased the framework's flexibility and adaptability to diverse data characteristics.

Conclusions:

  • Adopting a comprehensive modeling approach is vital for accurate economic evaluations.
  • A Bayesian framework offers strategic advantages for building and managing complex economic models.
  • This methodology enhances the reliability of resource allocation decisions in healthcare.