Research in the Centre is focussed around several research themes relating to the development and application of Bayesian statistics in health economics.
Bayesian Evidence Synthesis and Meta-Analysis
Economic evaluations require a synthesis of all available evidence into probability distributions for the input parameters. Bayesian evidence synthesis and meta-analysis provides a natural approach to representing uncertainty on input parameters. In addition, Bayesian methods allow a natural middle path to be taken between assuming that effects in different studies are known precisely or completely unknown, acknowledging that studies are related but not identical.
Elicitation of Expert Probabilities
It is not always possible to gather sufficient data to be able to ignore prior knowledge. Uncertain model input parameters are often assigned probability distributions entirely on the basis of expert judgments. The challenge is then to find appropriate questions to ask the expert to extract their knowledge, and then to determine a suitable probabilistic description of the variables we are interested in based on the information we have learned from them.
Extrapolation of hazard and survival function
Randomised controlled trials rarely take a lifetime perspective and there will often be some patients whose time-to-event is censored. Choosing an appropriate hazard function and incorporating all sources of uncertainty is important when estimating population mean time-to-event.
Modelling Health State Preference Data
CHEBS has been looking at an alternative approach to modelling health state values using a (nonparametric) Bayesian method and comparing this to conventional regression-based methods.
Modelling uncertainty in complex models
The MUCM project was concerned with addressing all sources of uncertainty in model predictions and quantifying their implications efficiently.
Value of Information Analysis to Identify Research Priorities
Value of information analysis provides a mechanism for identifying which parameters in an economic evaluation are contributing most to the overall decision uncertainty so that research can be prioritised accordingly.
CHEBS' current focus to quantifying structural uncertainty in health economic decision models is to build on the discrepancy modelling research initiated by the Managing Uncertainty in Complex Models (MUCM) project.
Meta-modelling involves the use of statistical regression to learn the model input-output relationship. A popular regression method used in meta-modelling involves the use of Gaussian processes.
Statistical Issues in Clinical Trials with Economic Outcomes (such as missing data and censoring)
It is increasingly common for clinical trials to collect data on health outcomes and costs to evaluate cost-effectiveness. Cost data typically exhibit highly skew distributions. Two techniques whose validity does not depend on any specific form of underlying distribution are the bootstrap and methods based on asymptotic normality of sample means. Although these methods are technically valid, it is important to apply methods that recognise the skewness in cost data to maximise efficiency and avoid misleading inferences. CHEBS has developed a general Bayesian framework for cost-effectiveness analysis allowing for very flexible modelling of both cost and efficacy.
Bayesian Clinical Trial Simulation (BCTS)
Bayesian clinical trial simulation differs from a conventional clinical trial simulation in that it acknowledges parameter uncertainty. Assurance is used to describe the unconditional probability of a successful clinical trial based on multiple outcomes.