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Area of Science:

  • Health Economics
  • Biostatistics
  • Observational Studies

Background:

  • Unmeasured confounding is a significant challenge in observational research.
  • Failure to address it can lead to biased estimates and unreliable statistical inferences.
  • Cost-effectiveness analyses (CEAs) are particularly vulnerable to unmeasured confounding.

Purpose of the Study:

  • To demonstrate the impact of unmeasured confounding on CEAs using observational data.
  • To propose and evaluate a Bayesian approach to correct for unmeasured confounding in CEAs.
  • To determine the necessary validation data size for valid inferences.

Main Methods:

  • Development of a Bayesian correction method for CEAs.
  • Consideration of different distributional assumptions for cost (normal, gamma) and effectiveness (normal).
  • Simulation studies to assess bias from ignoring confounders and the impact of validation data size.

Main Results:

  • Ignoring unmeasured confounders can significantly bias cost-effectiveness estimates.
  • The proposed Bayesian method, with available validation data, can correct for such biases.
  • Simulation results indicate the required size of validation data for accurate correction.

Conclusions:

  • Unmeasured confounding poses a substantial threat to the validity of observational CEAs.
  • A Bayesian approach utilizing validation data offers a viable strategy to mitigate bias.
  • Careful consideration of validation data requirements is crucial for reliable cost-effectiveness research.