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

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.