Bayesian approach to complex evidence synthesis with medical applications

F3.mediumThis project is concerned with the development and application of statistical methods to combine evidence from clinical trials, to produce more reliable estimates, making informed and evidence-based decision for health.

Most health technology assessment requires making use of all available evidence. A meta-analysis is often conducted to obtain an overall effect estimate by combining results from clinical trials that answer similar research questions. There is growing awareness that parameters in the random-effect meta-analysis model are often imprecisely estimated. Our recent work on constructing predictive distributions from Cochrane Database, demonstrates that using informative priors leads to more precise inference.

This project is envisaged to encompass both methodologies and their medical applications. We propose to extend current Bayesian methods from univariate meta-analysis to multivariate meta-analysis, to allow for simultaneous comparison of all treatment options. We will develop methods to include informative prior information into multivariate meta-analysis. This will result in improved precision in estimation, leading to clearer clinical decisions. Key considerations will be to make best use of all available evidence, informative priors, and produce treatment ranking and its uncertainty for each outcome. The proposed methods will be applied to real-life clinical examples.

Supervisor: Dr Yinghui Wei

 

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