ArticlePharmacoEconomics2020
Bayesian Hierarchical Models for Meta-Analysis of Quality-of-Life Outcomes: An Application in Multimorbidity.
Article in PharmacoEconomics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Comorbidity and health-related quality of life in people with a chronic medical condition in randomised clinical trials: An individual participant data meta-analysis.PLoS medicine · 2023Pooled it
- A Review of Methods for Research Synthesis.Statistics in medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHealth-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.
objectiveUsing 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.
methodsWe 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.
resultsOur 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.
conclusionBayesian hierarchical models may provide a useful modelling approach to systematically synthesize data from HRQoL studies.
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Registered trials
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