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Linear mixed models to handle missing at random data in trial-based economic evaluations.

Andrea Gabrio1, Catrin Plumpton2, Sube Banerjee3

  • 1Department of Methodology and Statistics, Faculty of Health Medicine and Life Science, Maastricht University, Maastricht, The Netherlands.

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Summary
This summary is machine-generated.

Linear mixed effects models (LMMs) provide a robust method for handling missing data in health economic evaluations. This approach offers a simpler alternative to multiple imputation in cost-effectiveness analyses (CEAs).

Keywords:
cost-effectiveness analysismissing datamixed-effectsrandomized trialrepeated measures model

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Area of Science:

  • Health Economics
  • Biostatistics
  • Clinical Trial Analysis

Background:

  • Trial-based cost-effectiveness analyses (CEAs) are crucial for evaluating health interventions.
  • Missing data in CEAs can lead to biased and inefficient estimates if not handled properly.
  • Multiple imputation is a common method for addressing missing data under the Missing At Random (MAR) assumption.

Purpose of the Study:

  • To introduce Linear Mixed Effects Models (LMMs) as a method for handling missing data in CEAs.
  • To demonstrate the implementation of LMMs in the context of cost-effectiveness analysis.
  • To encourage the adoption of LMMs over less adequate methods for missing data.

Main Methods:

  • Exploration of Linear Mixed Effects Models (LMMs) for handling missing data in CEAs.
  • Application of LMMs to a randomized trial of antidepressants.
  • Provision of implementation code for LMMs in R and Stata statistical software.

Main Results:

  • LMMs offer a straightforward approach to managing missing data under the MAR assumption without imputation.
  • The study illustrates the practical application of LMMs in a real-world CEA scenario.
  • Code examples facilitate the adoption of LMMs by practitioners.

Conclusions:

  • LMMs present a viable and potentially more accessible alternative to multiple imputation for missing data in CEAs.
  • Increased familiarity with LMMs may lead to improved analytical practices in health economic evaluations.
  • The study advocates for the wider implementation of LMMs to enhance the reliability of CEA findings.