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Cost-effectiveness analysis under multiple effectiveness outcomes: A probabilistic approach.

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  • 1Center for Data, Mathematical & Computational Sciences, Goucher College, Baltimore, Maryland, USA.

Statistics in Medicine
|July 4, 2023
PubMed
Summary
This summary is machine-generated.

New probability-based criteria assess cost-effectiveness for multiple health outcomes. These methods offer flexibility for policymakers, aiding in new treatment evaluations compared to standard care.

Keywords:
U-statisticscost-effectiveness probability (CEP)lower confidence limitparametric bootstrap

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Area of Science:

  • Health economics
  • Biostatistics
  • Clinical trial analysis

Background:

  • Evaluating new treatments requires assessing cost-effectiveness, especially with multiple health outcome measures.
  • Standard methods may not adequately capture complex trade-offs between cost and multiple effectiveness metrics.

Purpose of the Study:

  • To propose and investigate novel probability-based criteria for assessing the cost-effectiveness of new treatments against standard treatments.
  • To develop flexible metrics that incorporate policy maker preferences and thresholds for cost and effectiveness.

Main Methods:

  • Developed two conditional probability metrics: one for increased effectiveness at lower costs, another for lower costs with greater health benefits.
  • Utilized a percentile bootstrap approach for parametric confidence limits, assuming multivariate normality.
  • Employed a non-parametric estimation procedure based on U-statistics theory.

Main Results:

  • Proposed confidence limits demonstrated accurate maintenance of coverage probabilities in numerical simulations.
  • The methodologies were successfully illustrated using a case study on type two diabetes treatment.

Conclusions:

  • The developed probability-based criteria provide a flexible framework for policy makers to evaluate new treatments with multiple effectiveness measures.
  • The statistical methodologies offer robust tools for cost-effectiveness analysis in healthcare decision-making.