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- Title
Bayesian Hierarchical Models for Meta-Analysis of Quality-of-Life Outcomes: An Application in Multimorbidity.
- Authors
Schmitz, Susanne; Makovski, Tatjana T.; Adams, Roisin; van den Akker, Marjan; Stranges, Saverio; Zeegers, Maurice P.
- Abstract
<bold>Background: </bold>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.<bold>Objective: </bold>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.<bold>Methods: </bold>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.<bold>Results: </bold>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.<bold>Conclusion: </bold>Bayesian hierarchical models may provide a useful modelling approach to systematically synthesize data from HRQoL studies.
- Subjects
COMORBIDITY; META-analysis; COST effectiveness; QUALITY of life; UNCERTAINTY; STATISTICAL models; PROBABILITY theory
- Publication
PharmacoEconomics, 2020, Vol 38, Issue 1, p85
- ISSN
1170-7690
- Publication type
journal article
- DOI
10.1007/s40273-019-00843-z