Susanne Schmitz1, Tatjana T Makovski2,3,4, Roisin Adams5, Marjan van den Akker3,6,7, Saverio Stranges2,8,9, Maurice P Zeegers4. 1. Competence Center for Methodology and Statistics, Department of Population Health, Luxembourg Institute of Health, 1 A-B, rue Thomas Edison, 1445, Strassen, Luxembourg. susanne.schmitz@lih.lu. 2. Epidemiology and Public Health Research Unit, Department of Population Health, Luxembourg Institute of Health, Strassen, Luxembourg. 3. Department of Family Medicine, Care and Public Health Research Institute (CAPHRI), Maastricht University, Maastricht, The Netherlands. 4. Chairgroup of Complex Genetics and Epidemiology, Nutrition and Metabolism in Translational Research (NUTRIM), Care and Public Health Research Institute (CAPHRI), Maastricht University, Maastricht, The Netherlands. 5. National Centre for Pharmacoeconomics, St James's Hospital, Dublin, Ireland. 6. Institute of General Practice, Johann Wolfgang Goethe University, Frankfurt am Main, Germany. 7. Academic Centre of General Practice, Department of Public Health and Primary Care, KU Leuven, Leuven, Belgium. 8. Department of Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada. 9. Department of Family Medicine, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada.
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.
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.
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