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Related Experiment Videos

Handling missing data in patient-level cost-effectiveness analysis alongside randomised clinical trials.

Andrea Manca1, Stephen Palmer

  • 1Centre for Health Economics, University of York, York, UK. am126@york.ac.uk

Applied Health Economics and Health Policy
|September 16, 2005
PubMed
Summary

Handling missing data in cost-effectiveness analysis is crucial. Multiple imputation is recommended to accurately reflect uncertainty and improve the validity of trial-based economic evaluations.

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Area of Science:

  • Health Economics
  • Biostatistics
  • Clinical Trials

Background:

  • Missing data is a significant challenge in trial-based cost-effectiveness analyses (CEAs).
  • Traditional methods like case deletion and mean imputation have limitations.
  • Advanced techniques such as multiple imputation offer more robust solutions.

Purpose of the Study:

  • To describe common quantitative methods for handling missing data in CEAs.
  • To demonstrate the impact of different missing data handling approaches on CEA results using case studies.

Main Methods:

  • Sensitivity analysis of two trial-based economic evaluations using various missing data techniques.
  • Utilizing a statistical framework based on net benefits and cost-effectiveness acceptability curves to represent uncertainty.

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Main Results:

  • The choice of missing data handling strategy can significantly impact CEA results.
  • One case study showed sensitivity to imputation decisions and strategies, while the other did not.

Conclusions:

  • Explicit reporting of missing data handling strategies in CEAs is essential.
  • Multiple imputation is generally recommended to account for uncertainty in study results.