Development of a fully Bayesian framework for the identification and estimation of subgroup effects in Randomised Controlled Trials

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Population health can be improved by identifying treatments that work particularly well for individual patients. This is commonly described as stratified, precision, or personalised medicine. Personalised medicine is underpinned by subgroup analyses where the effectiveness of treatments are assess in subsets of study patients with defined characteristics. However, we need to account for the effects of chance, if we compare treatment response in a large number of subgroups we are likely to observe differences that arise purely by chance. For example, a study reported in 2005 listed ten cases where apparently clinically important subgroup effects were subsequently been shown to be false, such as the claim that aspirin for secondary prevention of stroke was ineffective in women. Unreliable subgroup analysis can lead to patients suffering harm, the waste of scarce resources, and futile confirmatory trials. The conventional approach is to advise caution, apply qualitative checklists to assess credibility, and apply statistical corrections. However, these approaches do not directly address the biases caused by multiplicity and leave the burden on the decision-maker to balance the risk of accepting "spurious" subgroup effects against the risk of rejecting true subgroup effects. This challenge can be seen in the FDA deliberations over the PLATO trial, where a treatment effect appeared to reverse in North American patients, and the differing decisions made by NICE and IQ-WiG based on subgroup analysis from the CAPRIE trial. Prior plausibility is identified as an important criterion for the credibility subgroup effects, but there is little guidance regarding how and when these judgements should be elicited. They are rarely elicited and recorded before trials commence, leaving decision-makers attempting to judge plausibility after trial results have been published. In this project, we will develop a fully Bayesian framework for subgroup analysis comprising a statistical model that adjusts for the effects of multiple testing and a framework for elicitation of judgements regarding the plausibility of subgroup effects. Prior plausibility is an important criterion for judging the credibility of subgroup effects but there is little guidance as to how and when plausibility should be assessed and reported. Importantly the statistical model will allow for the observed subgroup effects to represent a mixture of "true" reproducible subgroups and spurious effects caused purely by random chance. The Bayesian model will provide a direct estimate of the probability that a subgroup effect occurred by chance and will also be able to incorporate informative priors representing belief in the plausibility of subgroup effects. The framework will be tested in a number of real life case studies representing study design, commissioning of confirmatory studies based on subgroup analysis, and technology appraisal. Guidance on the use of the framework, to facilitate translation, will be developed based on the case studies.