Assessing the feasibility of complex and innovative trial designs

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A wide variety of complex and innovative designs (CIDs), which move beyond the standard randomised controlled trial, have been proposed recently. These designs have the potential to make clinical trials faster and more efficient, thereby increasing the speed of drug development and delivering better treatments to patients sooner. The novelty of these designs, however, has led to substantial uncertainty about how feasible they are to run in practice. Perennial concerns about poor recruitment, adherence and follow-up in clinical trials may be further amplified when using complex designs, while features specific to CIDs such as interim analyses, multiple randomisations, biomarker guided stratification, and the adding or removal of treatment arms, contribute further uncertainty. A quantitative approach to modelling these complex designs and assessing their feasibility could help avoid expensive and time-consuming infeasible trials whilst identifying promising situations which warrant confident investment. This project will initially set out to understand the key attributes of a CID in relation to its feasibility, in consultation with trial managers, methodologists, and statisticians. Appropriate methods for modelling trial processes and outcomes will then be reviewed and extended to the case of CIDs, before being applied to two real trial case studies of CIDs in oncology. Three methodological questions will then be addressed: a) how should we use models of CIDs to inform their design, in terms of both simple (e.g. stop/go) and complex (e.g. trial process optimisation) trial design decisions; b) how should we use data from early phase, pilot, or historical trials to calibrate CID trial models and thereby improve their predictions; and c) how can we analyse CID models to better understand the key areas of uncertainty which should be targeted in early-phase / pilot work. Although a Bayesian framework may be particularly appropriate given the focus on prediction and decision making, a frequentist approach may also be explored. The fact that CIDs typically require simulation to determine their statistical properties suggests that the developed methods will be computationally demanding, and so the project will be underpinned by a focus on the implementation and dissemination of efficient, freely-available and well-documented software packages which will facilitate their application in practice.