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

Susanne Schmitz (), Tatjana T. Makovski, Roisin Adams, Marjan Akker, Saverio Stranges and Maurice P. Zeegers
Additional contact information
Susanne Schmitz: Luxembourg Institute of Health
Tatjana T. Makovski: Luxembourg Institute of Health
Roisin Adams: St James’s Hospital
Marjan Akker: Maastricht University
Saverio Stranges: Luxembourg Institute of Health
Maurice P. Zeegers: Nutrition and Metabolism in Translational Research (NUTRIM), Care and Public Health Research Institute (CAPHRI), Maastricht University

PharmacoEconomics, 2020, vol. 38, issue 1, No 7, 85-95

Abstract: 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.

Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s40273-019-00843-z Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:pharme:v:38:y:2020:i:1:d:10.1007_s40273-019-00843-z

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/40273

DOI: 10.1007/s40273-019-00843-z

Access Statistics for this article

PharmacoEconomics is currently edited by Timothy Wrightson and Christopher I. Carswell

More articles in PharmacoEconomics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().