Title: Modelling Treatment Mechanism for Stratified Medicine in Randomised Clinical Trials
Funder: Department of Health and Social Care

Abstract: Background The development of stratified medicine is dependent on an understanding of treatment effect mechanisms and treatment moderators (interactions of treatment with other variables). This project is not concerned with the exploratory search for moderators commonly encountered in bioinformatics. The primary concerns here are to explore the two statistical themes of optimising formal statistical moderation models and how the combining of moderators can lead to more efficient estimation. This will enable the quantification of mechanistic treatment processes resulting in improved clinical understanding and the consideration of the best approach to communicate the results in practice. A combined measure has the potential to moderate the effect of an outcome more strongly than a single moderator. Repeated measures of a variable in a trial can be exploited to account for measurement error in the baseline moderator. Sequential moderation involving time varying moderators may be measured during or post treatment and requires special statistical consideration. Process models can be applied to multi-component biological/psychological models to identify predictors of different elements of treatment response. There is a need for a review and development of current statistical methods for the evaluation of treatment prediction. Aims To investigate statistical methods for candidate mechanisms of treatment moderation for advancing the operationalisation of stratified medicine To enhance patient and clinical communication: investigating how to convey findings in a way that allows informed patient choice Plan of Investigation / Design of Research The main focus of this research is concerned with secondary analyses of data from randomised trials to assess the evidence for moderation and advance understanding of treatment mechanisms. This research will involve three steps: To review, apply, test and develop the moderator methodology in simulated trial datasets. I will acquire skills in programming, model building and estimation, and simulation. I will utilise simulation experiments to explore how the statistical models behave. Following the statistical simulation work, I will focus on the application of the statistical models in real datasets. At this point I will have a clear idea of the properties of the methodology and can explore their usefulness in retrospective trial analyses and as part of modern biomarker stratified trial design. I will apply this methodology to several completed and currently recruiting trials on which I have worked or am familiar. Alongside (1) and (2) I will work with clinicians, patients and public groups to explore the best ways to present the findings to enable better patient understanding, and clear and appropriate interpretation of results in the clinical setting. Expected Outcomes These methods will be used to study moderators of treatment effects. The results will validate clinical markers in the test datasets and provide support for methodology to be used in future trial designs. Benefits Progress in identifying effective treatments personalised to the individual patient and understanding of treatment response variation depends upon the development of robust statistical methods to analyse and validate moderators of treatment outcome. Furthermore, the identification of appropriate statistical research methods for moderation will improve the analyses and reporting of treatment prediction and marker validation. It is hoped in the future that this will lead to more targeted treatments and the advance of practical stratified medicine.

Lay abstract: Aims of the research The title of this research is Modelling Treatment Mechanism for Stratified Medicine in Randomised Clinical Trials. I am a statistician and this research is concerned with the analyses of randomised clinical trials for stratified (or personalised) medicine. This is the targeting of treatment to a person's specific clinical characteristics, also called moderators. One way to identify potential predictors of treatment response is through analyses of randomised clinical trials, the gold standard for evidence based medicine. This research will enable randomised clinical trials to better evaluate predictors of response and more specifically, and how these predictors can be modelled in different circumstances. There are two aims to this research: To investigate statistical methods for evaluating candidate mechanisms of treatment moderation for advancing the implementation of stratified medicine in clinical practice. To enhance patient and clinical communication: investigating how to convey findings in a way that allows informed patient choice Background to this researchIt is thought that some antidepressants work better in people who have had no previous trauma in their lifetime while psychotherapy is more effective for those with childhood trauma. There are many examples of such moderators in clinical research but we do not always understand why some treatments are more effective. This project is less concerned with the exploratory search for moderators. Instead, I am looking at how best to model moderators that have already been highlighted as biologically or clinically plausible. Key to this to this enterprise is how best to assess, use and communicate this information. There are problems with the current methodology for stratified medicine. Issues such as missing data, measurement error and multiple testing need to be addressed. Moderators are generally measured as individual variables and there needs careful consideration as to how to incorporate these variables in the analysis of clinical trials. A real moderator may not have been measured in the trial or be an unobserved variable and I will examine methodology that addresses this. Recently methods combining predictors of treatment have become available. A combined moderator may be more powerful than individual variables and this research will assess when this might be the case. Design and Methods The primary concerns here are exploring the two methdological themes of how best to construct statistical models and the combining of measured moderators and moderation indices. This will enable efficient estimation and improved understanding of the mechanisms of the treatment process, and to consider the best approach to communicate the results in practice. Model Structure examines how to set up the statistical model for optimal evaluation of treatment moderation. Under the theme, Combining Moderators, I will study candidate mechanisms of treatment moderation in clinical research. Both themes will make use of simulated data to examine model performance with data where we know the underlying moderation mechanism, before selecting approaches for application to real trial data. Public and Patient InvolvementThis study will help in achieving improved targeting of treatment, better design of trials and improved use of the information gained to determine more rapidly appropriate treatment choices for patients. I will review previous work and discuss with patients and clinicians how strength of evidence for treatment choice can be most helpfully presented. I will then incorporate these findings into the various simulation and study scenarios and models described above. Dissemination Dissemination of my results will be at public events, such as the Cambridge Science Festival, at user group meetings, as well as in peer review statistical and clinical publications and scientific conferences. I plan, over the course of this research, to work alongside the King's Clinical Trials Unit using my findings to inform the design of new trials and the analysis of current recruiting trials.

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