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

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