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Flexible Bayesian longitudinal models for cost-effectiveness analyses with informative missing data.

Alexina J Mason1, Manuel Gomes2, James Carpenter3,4

  • 1Department of Health Services Research and Policy, LSHTM, University of London, London, UK.

Health Economics
|September 25, 2021
PubMed
Summary
This summary is machine-generated.

Cost-effectiveness analyses (CEA) require robust methods for handling complex missing longitudinal data, including interim missingness and loss to follow-up. New Bayesian models effectively address these challenges, improving sensitivity analyses for health economic evaluations.

Keywords:
Bayesian analysiscost-effectiveness analysismissing not at randomselection modelsensitivity analysis

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Area of Science:

  • Health economics
  • Biostatistics
  • Longitudinal data analysis

Background:

  • Cost-effectiveness analyses (CEA) commonly use sensitivity analyses to address missing data.
  • Longitudinal health data often exhibit complex missingness patterns, including interim missingness and loss to follow-up.
  • Existing methods for CEA do not adequately address these complex missing data patterns, especially when data are missing not at random.

Purpose of the Study:

  • To develop flexible Bayesian longitudinal models for handling complex missing data in CEA.
  • To enable sensitivity analyses that account for various missing data mechanisms.
  • To jointly model correlated, non-normally distributed costs and health outcomes.

Main Methods:

  • Development of Bayesian longitudinal models to disentangle interim missingness and loss to follow-up.
  • Joint modeling of costs and health-related quality of life (HRQoL) outcomes.
  • Application of the models to the REFLUX study with 52% missing HRQoL data.

Main Results:

  • The proposed models can accommodate complex missing data mechanisms in longitudinal CEA.
  • The framework allows for joint modeling of costs and outcomes, accounting for their correlation and non-normal distributions.
  • Demonstrated utility in the REFLUX study, providing a template for future research.

Conclusions:

  • Flexible Bayesian longitudinal models offer a robust approach to handling complex missing data in CEA.
  • These methods enhance the reliability of sensitivity analyses in health economic evaluations.
  • Guidance and code are provided to facilitate adoption in future studies.