Innovative method | Summary |
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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. |