Title: Bayesian methods, health technology assessment, and performance monitoring
Funder: Medical Research Council

Abstract: The overall aim of this research programme is the provision of formal tools to help decision-making in the face of multiple sources of quantitative evidence, taking appropriate account of inadequacies and potential conflicts in available data. The underlying methodological themes explored in this programme apply whether the decision concerns, say, the approval of a new intervention by NICE, or which NHS Trust to inspect. Apart from development of the formal methods, there will be strong emphasis on dissemination to a wider audience and the provision of guidance for appropriate use. Three primary themes continue: First, research into Bayesian methods and applications will focus on improving the practice and widening the scope of Bayesian analysis in general. Model comparison is an essential component, and we shall try to refine the Deviance Information Criterion (DIC), which is becoming a standard tool in Bayesian applications. Other generic research for improving the robustness of Bayesian modelling includes a methodology for measurement of conflict in a complex graphical model, identification of prior distributions that are unintentionally informative, and tools for helping assess informative prior distributions. In addition, we shall work on extending standard modelling to include a parametric Huber distribution with a normal core and exponential tails, which appears appropriate for the analysis of micro-array data, and exploring the potential for improving robustness by cutting feedback in a full-probability model. All these innovations will be implemented in BUGS, the MCMC software that dominates applied Bayesian modelling. This is now developing as an Open Source project, and work in the Unit will focus on dissemination through articles, a tutorial book and courses. The second major theme will focus on performance monitoring in health services, motivated by the close relationship with the Healthcare Commission. Substantial statistical innovation is taking place in the Healthcare Commission in collaboration with the Unit concerning comparison, screening, surveillance and rating of organisations, and the refinement and dissemination of these methods will be essential. These use fairly basic (non-Bayesian) statistical methods, but introducing the concept of false discovery rates (FDR) appears appropriate. There will be parallel development of more sophisticated methods for monitoring multiple institutions, building on earlier work on risk-adjusted Exponentially Weighted Moving Averages (RA-EWMAs) and multiple CUSUms The final theme will concern the application of Bayesian ideas in healthcare evaluation, including work on adaptive studies, safety monitoring, and sensitivity analysis in complex modelling. However the primary effort will be in developing a robust approach to the quantitative assessment of possible biases in studies, both internal (lack of rigour/quality) and external (lack of relevance/generalisability). This is essentially an exercise in guided prior elicitation, but should result in a way of automatically down-weighting studies in the synthesis of evidence by bodies such as NICE. If successful, this procedure could have a major impact in the process by which evidence is used in policy decisions.


Research activity codes: 8.4 RESEARCH DESIGN AND METHODOLOGIES (HEALTH SERVICES)
Health categories: GENERIC HEALTH RELEVANCE