Bayesian Meta-Analysis Of Diverse Sources Of Evidence Can Expedite Health Technology Assessments

Leicester, United Kingdom – In health technology assessment, decisions about the availability of new health technologies within a national health service are largely based on effectiveness estimates. Variability in reported outcomes measured by alternative instruments, or extended follow-up time of clinical trials may, however, lead to limited data availability when early evaluation is based on a single target outcome. Limited data may result in large uncertainty around the effectiveness estimate and an ultimately inefficient decision-making process.

The use of diverse sources of evidence, such as outcomes measured by multiple instruments or surrogate endpoints or external information, can improve estimation.

In the article “Use of Bayesian Multivariate Meta-Analysis to Estimate the HAQ for Mapping Onto the EQ-5D Questionnaire in Rheumatoid Arthritis,” published in Value in Health, the researchers describe how multivariate meta-analysis can be used to synthesize data on multiple outcomes, which can include multiple instruments or surrogate endpoints (measured early, less invasively or less costly). Additional external data, for example from relevant studies or based on experts’ opinions, can be incorporated in the evidence synthesis by implementing it in a Bayesian framework in the form of so-called prior distributions. This approach can not only help to ensure that no information is ignored, but can also lead to better estimation of clinical effectiveness and consequently, the estimation of the health-related quality of life outcomes so important in the economic evaluation.

“By allowing all relevant data to be incorporated in estimating clinical effectiveness, multivariate meta-analysis can improve the estimation. Reduced uncertainty may make it easier to carry out economic evaluations of new health technologies, and the use of short-term surrogate endpoints could lead to earlier decisions,” said lead author, Dr. Sylwia Bujkiewicz, lecturer in the Biostatistics Research Group, University of Leicester.



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