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Bayesian Hierarchical Models for Meta-Analysis of Quality-of-Life Outcomes: An Application in Multimorbidity

Author

Listed:
  • Susanne Schmitz

    (Luxembourg Institute of Health)

  • Tatjana T. Makovski

    (Luxembourg Institute of Health
    Maastricht University
    Nutrition and Metabolism in Translational Research (NUTRIM), Care and Public Health Research Institute (CAPHRI), Maastricht University)

  • Roisin Adams

    (St James’s Hospital)

  • Marjan Akker

    (Maastricht University
    Johann Wolfgang Goethe University
    KU Leuven)

  • Saverio Stranges

    (Luxembourg Institute of Health
    Western University
    Western University)

  • Maurice P. Zeegers

    (Nutrition and Metabolism in Translational Research (NUTRIM), Care and Public Health Research Institute (CAPHRI), Maastricht University)

Abstract

Background Health-related quality of life (HRQoL) is a key outcome in cost-utility analyses, which are commonly used to inform healthcare decisions. Different instruments exist to evaluate HRQoL, however while some jurisdictions have a preferred system, no gold standard exists. Standard meta-analysis struggles with the variety of outcome measures, which may result in the exclusion of potentially relevant evidence. Objective Using a case study in multimorbidity, the objective of this analysis is to illustrate how a Bayesian hierarchical model can be used to combine data across different instruments. The outcome of interest is the slope relating HRQoL to the number of coexisting conditions. Methods We propose a three-level Bayesian hierarchical model to systematically include a large number of studies evaluating HRQoL using multiple instruments. Random effects assumptions yield instrument-level estimates benefitting from borrowing strength across the evidence base. This is particularly useful where little evidence is available for the outcome of choice for further evaluation. Results Our analysis estimated a reduction in quality of life of 3.8–4.1% per additional condition depending on HRQoL instrument. Uncertainty was reduced by approximately 80% for the instrument with the least evidence. Conclusion Bayesian hierarchical models may provide a useful modelling approach to systematically synthesize data from HRQoL studies.

Suggested Citation

  • Susanne Schmitz & Tatjana T. Makovski & Roisin Adams & Marjan Akker & Saverio Stranges & Maurice P. Zeegers, 2020. "Bayesian Hierarchical Models for Meta-Analysis of Quality-of-Life Outcomes: An Application in Multimorbidity," PharmacoEconomics, Springer, vol. 38(1), pages 85-95, January.
    • Handle: RePEc:spr:pharme:v:38:y:2020:i:1:d:10.1007_s40273-019-00843-z
    DOI: 10.1007/s40273-019-00843-z
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    References listed on IDEAS

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