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Missing data in trial-based cost-effectiveness analysis: An incomplete journey.

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Summary
This summary is machine-generated.

Missing data are common in trial-based cost-effectiveness analyses (CEA). Most studies use complete case analysis, with few exploring missing-at-random assumptions, indicating a need for improved methods in healthcare decision making.

Keywords:
cost-effectiveness analysismissing datamultiple imputationrandomised controlled trialssensitivity analysis

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Area of Science:

  • Health Economics
  • Biostatistics
  • Clinical Trials

Background:

  • Cost-effectiveness analyses (CEA) alongside randomized trials are crucial for healthcare decision-making.
  • Missing data present significant challenges in conducting robust trial-based CEAs.
  • Recent methodological advancements exist for handling missing data in clinical trials.

Discussion:

  • This review examines the prevalence and handling of missing data in trial-based CEAs published between 2013-2015.
  • Fifty-two studies were analyzed, revealing that missing cost-effectiveness data is widespread, with a median of 63% of participants having complete data.
  • The primary analysis predominantly used complete case analysis (43%), followed by multiple imputation (30%).

Key Insights:

  • A substantial proportion of trial participants lack complete cost-effectiveness data, impacting analysis validity.
  • Limited use of sensitivity analyses exploring departures from the missing-at-random assumption (only 4%) is a critical gap.
  • Current practices in addressing missing data in trial-based CEAs require significant improvement.

Outlook:

  • Future research should focus on minimizing missing data within randomized trials.
  • Adoption of appropriate primary analysis methods, valid under plausible assumptions, is essential.
  • Enhanced sensitivity analyses, particularly concerning the missing-at-random assumption, are recommended for rigorous CEA.