, Volume 38, Issue 1, pp 85–95 | Cite as

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

  • Susanne SchmitzEmail author
  • Tatjana T. Makovski
  • Roisin Adams
  • Marjan van den Akker
  • Saverio Stranges
  • Maurice P. Zeegers
Original Research Article



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.


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.


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.


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.


Bayesian hierarchical models may provide a useful modelling approach to systematically synthesize data from HRQoL studies.


Author Contributions

SSc extracted the data, conducted the statistical analysis, interpreted the results, and wrote the first draft of the manuscript. TM conducted the literature review, extracted and checked the data, and provided detailed feedback on the manuscript. RA provided important input on the application of HRQoL in CUA, and provided detailed feedback on the manuscript. MvA extracted data and provided feedback on the manuscript. SSt and MZ reviewed and provided important feedback both during the analysis phase and for the manuscript.

Compliance with Ethical Standards


No funding was received to conduct this study.

Conflict of interest

Susanne Schmitz, Tatjana Makovski, Roisin Adams, Marjan van den Akker, Saverio Stranges, and Maurice P. Zeegers have no conflicts of interest to report in respect of this study.

Supplementary material

40273_2019_843_MOESM1_ESM.pdf (494 kb)
Supplementary material 1 (PDF 494 kb)
40273_2019_843_MOESM2_ESM.pdf (429 kb)
Supplementary material 2 (PDF 429 kb)
40273_2019_843_MOESM3_ESM.pdf (753 kb)
Supplementary material 3 (PDF 753 kb)


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Competence Center for Methodology and Statistics, Department of Population HealthLuxembourg Institute of HealthStrassenLuxembourg
  2. 2.Epidemiology and Public Health Research Unit, Department of Population HealthLuxembourg Institute of HealthStrassenLuxembourg
  3. 3.Department of Family Medicine, Care and Public Health Research Institute (CAPHRI)Maastricht UniversityMaastrichtThe Netherlands
  4. 4.Chairgroup of Complex Genetics and EpidemiologyNutrition and Metabolism in Translational Research (NUTRIM), Care and Public Health Research Institute (CAPHRI), Maastricht UniversityMaastrichtThe Netherlands
  5. 5.National Centre for PharmacoeconomicsSt James’s HospitalDublinIreland
  6. 6.Institute of General PracticeJohann Wolfgang Goethe UniversityFrankfurt am MainGermany
  7. 7.Academic Centre of General Practice, Department of Public Health and Primary CareKU LeuvenLeuvenBelgium
  8. 8.Department of Epidemiology and Biostatistics, Schulich School of Medicine and DentistryWestern UniversityLondonCanada
  9. 9.Department of Family Medicine, Schulich School of Medicine and DentistryWestern UniversityLondonCanada

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