Title: Learning and clustering: combining adjustments for the learning curve and clustering effects in randomised surgical trials
Funder: Department of Health and Social Care

Abstract: Randomised controlled trials (RCTs) are considered the highest level of evidence. Consequently, it is important to ensure that they are designed, conducted and analysed to the highest possible rigour so that the clinical decisions that they are used to inform are valid. There are many practical and methodological difficulties that a medical researcher must overcome to conduct a successful RCT. In trials with a surgical intervention, these difficulties are often magnified. However, there are also additional challenges specific to surgical trials. This project focuses on two highly prevalent challenges: Surgical learning curve Clustering within site and surgeon Statistical methods have been developed with low uptake to account for the surgical learning curve and clustering effects separately in surgical trials. This research will evaluate these and determine when and how to apply them, by considering these methodologies individually and then jointly. The ultimate aim of this project is to improve the design, analysis, and generalizability of surgical trials. This will be achieved by developing understanding about when learning curves and clustering effects impact on the conclusions of an RCT by determining; the available methodologies and the impact of incorporating these methodologies. This will inform the impact of adjusting for these effects and their impact on interpretation. This will be done considering each effect in isolation and then in combination. This project has a number of phases each contributing to the overall research objective. Phase 1 will establish current practice by examining the literature to determine how surgical learning and clustering are accounted for in trial design or analysis, and by contacting statisticians and triallists, experienced in designing such studies, to gauge their awareness of methods for handling these considerations. Phase 2 will identify existing methodology that could be translated and applied to clinical trials data by searching within and outside of medical statistics literature. Phase 3 will explore the impact that accounting for each of these effects separately has on the conclusions drawn from a trial. Phase 4 will explore the feasibility of accounting for both effects simultaneously. Phases 3 and 4 will use real and simulated data. Phase 5 will make recommendations for practice based on this research and involve the development of guidance for statisticians and methodologists. This work will increase the awareness and accessibility of effective methodology and determine when it is appropriate to use them. In particular expanding methods to allow for joint adjustment of learning and clustering and provide advice on how to approach such analyses with the necessary consideration of both the clinical and statistical issues. This project has the potential to be widely adopted to ensure future surgical trials provide the best information to inform clinical decision making, ultimately improving patient care. Furthermore, there is scope for this research to inform other areas of medical research involving other complex interventions e.g. psychological therapies.

Lay abstract: Randomised controlled trials (RCTs) are considered the highest level of evidence. Consequently, it is important to ensure that they are designed, conducted and analysed to the highest possible standards so that the clinical decisions they are used to inform are valid. RCTs can be challenging to design and conduct. RCTs with a surgical intervention encounter additional challenges that may not be present in trials of other interventions. This project focuses on two such challenges, important in the design of surgical trials, which in practice are not considered at all or are only considered separately.The first challenge is called the learning curve. When a clinical trial starts, surgeons may not have the same level of experience of each surgical technique under examination. During the conduct of the trial, a surgeon's experience will develop and this will also impact on the outcomes for patients. This phenomenon is called the learning curve. The second challenge is called clustering. This happens when people within a particular group, as defined by the trial design, are more likely to have similar outcomes than people between groups. In this example people operated on by the same surgeon or within the same site may have similar outcomes due to training, expertise and experience. Statistical methods have been developed to account for these learning and clustering effects separately though have not been widely adopted in practice. I aim to evaluate these methodologies and determine when and how to apply them. I will first consider the methodologies individually and then jointly. Within this project I will examine the existing literature to consider how these challenges are accounted for in surgical trial design and analysis; statisticians and triallists who are experienced in designing such studies to gauge their awareness of these considerations; look outside the medical statistics literature to identify methodologies that could be translated and applied to clinical trials data; determine the best methods to use; and ultimately develop guidance for other statisticians to improve and standardise practice. The results of this project have the potential to be widely adopted to ensure future surgical trials provide the highest level of evidence to inform clinical decision making, ultimately improving patient care. There is further scope to inform other areas of medical research involving complex interventions e.g. psychological therapies.

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