|Dr. Shijie (Kate) Ren|
Dr. Shijie (Kate) Ren has published a paper with colleagues from the School of Mathematics and Statistics and HEDS proposing an elicitation framework to capture external evidence about heterogeneity for use in evidence synthesis with limited studies.
Their proposed framework allows uncertainty to be represented by a genuine prior distribution, using empirical evidence and experts’ beliefs, and can avoid making misleading inferences. The method is flexible to what judgments an expert is able to provide. They have also provided R code for implementing their method.
|© Sage Journals|
Dr. Ren, who specialises in the application of Bayesian methods in health care evaluation, argues that, “Analysts often default to use a fixed effect model in evidence synthesis because there are too few studies to conduct a random effect model. The choice of which model to use should depend on the objective of the analysis and knowledge of the included studies.”
In the case where heterogeneity is expected, the proposed elicitation framework can overcome the problem of imprecise estimates of the heterogeneity parameter in the absence of sufficient sample data.
The article, published in Medical Decision Making, is available open-access and can be found at http://dx.doi.org/10.1177/