Award Title: Trial design and analysis methods to incorporate the complexities of surgery - Evaluating the individual and combined effects of the constituent components of surgical interventions and the effects of surgical learning curves.
Funder: Department of Health and Social Care

Abstract: BACKGROUND: Current approaches to trial design and analysis for evaluating surgical interventions do not sufficiently account for the fact that surgery is a complex intervention. The trial designs/analyses often mirror pharmaceutical trials, answering black-and-white, 'A-vs-B'-style questions, without addressing specifically how the treatments should be performed, or how their effectiveness changes as the surgeons are learning them. This causes limited influence of trial results, perpetuation of the large-scale heterogeneity of surgical care delivery, the delayed roll-out of beneficial surgical techniques and the sub-optimal implementation of new interventions. A paradigm shift is required, moving beyond conventional parallel-group trial designs that ignore complexities of an intervention such as surgery. AIM: Enhance the value and impact of surgical trials by developing and extending existing surgical trial designs and analyses. OBJECTIVES: Review existing methodologies for trial design and analysis of complex interventions which can be used to meet the needs in the surgical setting for addressing learning effects and analysing the individual components of an intervention. Develop, evaluate and illustrate surgical trial designs able to estimate surgeon learning curves. Extend existing statistical methods to estimate the effects of mandatory and optional components of surgical interventions on patient outcomes in the context of conventional parallel-group designs. Develop, evaluate and illustrate trial designs for estimating the individual and combined effects of mandatory and optional intervention components on patient outcomes. Provide practical guidance for the use of these design and analysis methods, ensuring clear communication of results to non-statisticians, to maximise impact. METHODS: Qualitative synthesis of surgical literature to illustrate the need in the surgical setting for the proposed research. Targeted literature reviews and snowballing to relate these needs to relevant methodologies from design of experiments and causal inference. Design of experiments approach to develop a design incorporating learning effects, adapting the replicated-crossover design. Evaluate the design via simulation studies. Output methodological paper and software for implementation of new design. Extend causal inference methodologies to evaluate the effects of mandatory/optional components of a surgical intervention. Illustrate the methods on trial datasets. Output guidance paper and example code for statisticians. Same approach as that outlined in 2, but drawing from designs which use multiple, conditional randomisations. Workshop to discuss practical implications of 2-4. Output a practical guidance paper to augment the more methodological papers. IMPACT: The methodological papers and practical guidance will facilitate the uptake of the proposed designs and analyses in surgical trials by making them accessible to all stakeholders. Once in use, these designs and analyses will allow trials to be commenced sooner, to be more inclusive for surgeons and to produce a more detailed evidence base to inform surgeons/policymakers not only about which intervention leads to better patient outcomes on average, but also specific details of how best to deliver the intervention. This will lead to improved reproducibility of results in wider practice, faster and wider adoption of beneficial techniques, considerable reduction in the inequality of care that patients currently receive and the development of evidence-based, tailored interventions for different subpopulations of patients.

Lay Abstract: Background: Before a new surgical intervention can be adopted into practice, it must be determined that patients would benefit more from receiving the new intervention than receiving the current intervention. The randomised controlled trial (RCT) is accepted as the best approach for doing this. There are many different RCT designs, and surgical research has embraced the conventional 'parallel-group' design typically used to test pharmaceuticals. However, this design cannot handle the complex nature of a surgical intervention and so common features of surgery often render surgical RCTs invalid. Learning effects: One such feature is that surgeons need time to learn the new technique. The conventional RCT involves randomising a large number of patients from multiple hospitals to either receive a new technique ('Surgery A') or the current technique ('Surgery B'), and then comparing patient outcomes between the two groups. Many surgeons in the trial will be unfamiliar with 'Surgery A', and it is expected that for these surgeons there will be a period of learning during which time the technique will be less effective than it is when performed by an expert. In contrast, 'Surgery B' is typically a well-established technique that most of the surgeons in the trial will already be very familiar with. This can lead to an unfair comparison between a 'Surgery B' at full potential and a 'Surgery A' at less than full potential. Component effects: Another feature is that surgical interventions are made up of many different components (e.g. operating surgeon, the wider surgical team, the specific equipment available, the local policy on post-operative care, etc.). These components determine what version of the intervention a patient receives, and they can all vary considerably from patient to patient. Patients receiving 'Surgery A', for example, may all receive different antibiotic regimes depending on their hospital. Furthermore, these differences in technique can affect patient outcomes e.g. maybe certain antibiotic regimes for 'Surgery A' lead to lower rates of infection. We need to be able to perform analyses that evaluate whether different versions of an intervention lead to different patient outcomes and, if so, then which versions lead to the best outcomes. Aims: The first aim is to develop new RCT designs which can provide relevant, precise and fair comparisons between surgical interventions even when learning effects are present. The newly developed designs will be evaluated via simulation studies to see how capable they are of producing quality results under various common real-life scenarios. The second aim is to develop new RCT designs that allow for analyses that explore how choices of the components (e.g. which antibiotic regime to use) affect patient outcomes, and to explore how best to communicate the results from these more complex analyses. Analysis methods will be explored by using data from existing trials where lots of information about how the surgical interventions were delivered was collected. PPI: I will seek patient and surgeon input to my project relating to acceptability and practicality of the new RCT designs during their development. My advisory group, which will meet biannually, includes PPI and surgeon representatives. I will also have regular contact with surgeons and PPI representatives through my trials work at Leeds CTRU. Furthermore, I plan to hold two workshops to discuss the new designs with PPI groups and with surgeons. Outputs: Outputs from this project will include methodological papers and software for researchers to use the new designs and papers summarising practical considerations as discussed with PPI reps and surgeons. Dissemination will be through the surgical and UK clinical trials unit network, presentations and discussion at relevant surgical and methodological conferences, and the PPI workshop.

Research Activity Codes: 6.4 SURGERY
Health Categories: GENERIC HEALTH RELEVANCE