Use of Bayesian multivariate meta-analysis to estimate the HAQ for mapping onto the EQ-5D questionnaire in rheumatoid arthritis.
BACKGROUND
In health technology assessment, decisions about reimbursement for new health technologies are largely based on effectiveness estimates. Sometimes, however, the target effectiveness estimates are not readily available. This may be because many alternative instruments measuring these outcomes are being used (and not all always reported) or an extended follow-up time of clinical trials is needed to evaluate long-term end points, leading to the limited data on the target clinical outcome. In the areas of highest priority in health care, decisions are required to be made on a short time scale. Therefore, alternative clinical outcomes, including surrogate end points, are increasingly being considered for use in evidence synthesis as part of economic evaluation.


OBJECTIVE
To illustrate the potential effect of reduced uncertainty around the clinical outcome on the utility when estimating it from a multivariate meta-analysis.


METHODS
Bayesian multivariate meta-analysis has been used to synthesize data on correlated outcomes in rheumatoid arthritis and to incorporate external data in the model in the form of informative prior distributions. Estimates of Health Assessment Questionnaire were then mapped onto the health-related quality-of-life measure EuroQol five-dimensional questionnaire, and the effect was compared with mapping the Health Assessment Questionnaire obtained from the univariate approach.


RESULTS
The use of multivariate meta-analysis can lead to reduced uncertainty around the effectiveness parameter and ultimately uncertainty around the utility.


CONCLUSIONS
By allowing all the relevant data to be incorporated in estimating clinical effectiveness outcomes, multivariate meta-analysis can improve the estimation of health utilities estimated through mapping methods. While reduced uncertainty may have an effect on decisions based on economic evaluation of new health technologies, the use of short-term surrogate end points can allow for early decisions. More research is needed to determine the circumstances under which uncertainty is reduced.
Biostatistics Research Group, Department of Health Sciences, University of Leicester, Leicester, UK. Electronic address: sb309@le.ac.uk.
Sylwia
S
Bujkiewicz
Genetic Epidemiology Group, Department of Health Sciences, University of Leicester, Leicester, UK.
John R
JR
Thompson
Biostatistics Research Group, Department of Health Sciences, University of Leicester, Leicester, UK.
Alex J
AJ
Sutton
Biostatistics Research Group, Department of Health Sciences, University of Leicester, Leicester, UK.
Nicola J
NJ
Cooper
Manchester Centre for Health Economics, Institute of Population Health, The University of Manchester, Manchester, UK.
Mark J
MJ
Harrison
NIHR Manchester Musculoskeletal Biomedical Research Unit, Arthritis Research UK Epidemiology Unit, School of Translational Medicine, University of Manchester, Manchester, UK.
Deborah P M
DP
Symmons
Biostatistics Research Group, Department of Health Sciences, University of Leicester, Leicester, UK.
Keith R
KR
Abrams
D018501:Antirheumatic Agents / Q000627:therapeutic use*; D001172:Arthritis, Rheumatoid / Q000188:drug therapy*; D001499:Bayes Theorem; D006801:Humans; D015201:Meta-Analysis as Topic; D011788:Quality of Life; D011795:Surveys and Questionnaires; D013673:Technology Assessment, Biomedical
D018501:Antirheumatic Agents
Bayesian analysis; health technology assessment; meta-analysis; multiple end points; rheumatoid arthritis; surrogate end points