Table 4 Summary of innovative design and analysis approaches briefed in this paper

Adaptive designs

• Offer opportunity to make changes to the design of an ongoing trial as patient outcome data is accrued.

• Can improve efficiency of trial (more power for same sample size, or reduced sample size for same power), make trial more robust to design assumptions, and/or improve patient benefit provided by trial.

• Benefit relies on the primary endpoint (or informative intermediate endpoint) being observed relatively quickly compared to recruitment.

Adaptive signature design

• Uses high-dimensional data to form a ‘sensitive’ subgroup of patients who experience higher benefit from an intervention in comparison to the overall population.

• Allows forming, and confirmatory testing, of a predictive signature in the same trial.

• May be difficult to interpret the resulting signature.

Augmented analysis of composite responder outcomes

• Efficiently analyse responder endpoints, which classify patients as responders or non-responders on the basis of a combination of binary and continuous measurements.

• Can substantially improve the power of trials using responder endpoints whilst maintaining the clinically relevant outcome.

• More complex analysis that makes extra assumptions compared to the traditional analysis approach.

Basket and umbrella designs

• Use an overarching protocol to test interventions in related disease conditions or patient subgroups, simultaneously.

• Allow operational and statistical efficiencies; with the latter realised by using advanced statistical approaches that can e.g., share information between the different arms of the trial.

• Generally requires assuming the same endpoint and control group, despite various sub-studies, in the trial.

Emulation of trials

• A method for using large retrospective datasets to predict what it would have been if yielded by a randomised controlled trial.

• Exploits the value of data that is already collected.

• Analysis makes strong assumptions and can only compare interventions in current use.

Sequential Multi Assignment Randomised Trials (SMART)

• Allow multiple randomisations of patients at different stages of the study.

• Allow separate research questions to be answered and for the optimal ‘adaptive intervention’ to be found.

• For a specific AI, they allow to improve individual outcomes by further tailoring treatment by baseline or time-varying characteristics.