Randomised trials at the level of the individual

Abstract

In global health research, short-term, small-scale clinical trials with fixed, two-arm trial designs that generally do not allow for major changes throughout the trial are the most common study design. Building on the introductory paper of this Series, this paper discusses data-driven approaches to clinical trial research across several adaptive trial designs, as well as the master protocol framework that can help to harmonise clinical trial research efforts in global health research. We provide a general framework for more efficient trial research, and we discuss the importance of considering different study designs in the planning stage with statistical simulations. We conclude this second Series paper by discussing the methodological and operational complexity of adaptive trial designs and master protocols and the current funding challenges that could limit uptake of these approaches in global health research.

The economic challenges of new drug development

“…Another may be a greater recognition of the benefits of more efficient clinical trial designs. Important evidence on the effectiveness of multiple candidate COVID-19 treatments from the RECOVERY and REMAP-CAP trials highlights the potential efficiencies from adaptive platform trials with a common control group [ 19 , 20 ]. However, many COVID-19 trials have competed against each other for participants, been too small and had insufficient statistical power to detect meaningful treatment effects [ 20 ].…”

Design and analysis of umbrella trials: Where do we stand?

“…The idea of pooled sample size computation for umbrella trials is relatively uncommon in practice; only four trials (ACTG A5288, BATTLE-1&2, PANGEA), used a pooled approach ( 60 – 62 ). Since a generic approach to sample size computation that works for different umbrella designs is currently lacking from the literature, the use of simulation for umbrella trial design is often required.…”

An explainable machine learning-based phenomapping strategy for adaptive predictive enrichment in randomized controlled trials

“…5,6 With a growing pipeline of potential new therapeutics, there is a need to explore alternative methods of conducting RCTs to increase their efficiency and provide high-quality evidence to ensure patient safety and regulatory compliance. 7,8 We have recently described a machine learning (ML) method that leverages the phenotypic diversity of patients in RCTs and the random allocation of the intervention to define signatures of individualized treatment effects. [9][10][11] Our method is based on an approach that creates a multidimensional representation of an RCT population across all pre-randomization features ("phenomap") and extracts signatures that define consistent benefit or risk from each study arm.…”

“…5,6 With a growing pipeline of potential new therapeutics, there is a need to explore alternative methods of conducting RCTs to increase their efficiency and provide high-quality evidence to ensure patient safety and regulatory compliance. 7,8…”

“…5,6 With a growing pipeline of potential new therapeutics, there is a need to explore alternative methods of conducting RCTs which would increase their efficiency, maintain their robustness, and provide high-quality evidence to ensure patient safety and regulatory compliance. 7,8 Adaptive trials have been proposed as a potential solution, 9,10 as highlighted in a recent United States Food and Drug Administration (FDA) statement. 11 Adaptive trials allow prospectively planned modifications to the study design of clinical trials based on accumulating data from patients already enrolled in the trial.…”