Decision rules for identifying combination therapies in open‐entry, randomized controlled platform trials

Abstract

Platform trials have become increasingly popular for drug development programs, attracting interest from statisticians, clinicians and regulatory agencies.Many statistical questions related to designing platform trials-such as the impact of decision rules, sharing of information across cohorts, and allocation ratios on operating characteristics and error rates-remain unanswered. In many platform trials, the definition of error rates is not straightforward as classical error rate concepts are not applicable. For an open-entry, exploratory platform trial design comparing combination therapies to the respective monotherapies and standard-of-care, we define a set of error rates and operating characteristics and then use these to compare a set of design parameters under a range of simulation assumptions. When setting up the simulations, we aimed for realistic trial trajectories, such that for example, a priori we do not know the exact number of treatments that will be included over time in a specific simulation run as this follows a stochastic mechanism. Our results indicate that the method of data sharing, exact specification of decision rules and a priori assumptions regarding the treatment efficacy all strongly contribute to the operating characteristics of the platform trial. Furthermore, different operating characteristics might be of importance to different stakeholders. Together with the potential flexibility and complexity of a platform trial, which also impact the achieved operating characteristics via, for example, the degree of efficiency of data sharing this implies that utmost care needs to be given to evaluation of different assumptions and design parameters at the design stage.

SynergyFinder 3.0: an interactive analysis and consensus interpretation of multi-drug synergies across multiple samples

“…Multi-drug treatments can result in therapeutic benefits both by enhancing the treatment efficacy and by avoiding the acquisition of monotherapy resistance ( 7–10 ). Historically, drug combinations have been identified using a trial-and-error method that requires considerable time and may lead to sub-optimal results ( 7 , 11 , 12 ). High-throughput screening (HTS) technologies have enabled a more systematic and accelerated discovery of new drug combination candidates ( 4 , 9 , 13–15 ).…”

Designing an exploratory phase 2b platform trial in NASH with correlated, co-primary binary endpoints

“…Based on the proposed design, comprehensive simulations were run for two scenarios: monotherapy (one dose) versus control and combination therapy versus monotherapies versus control. We will present results of the former in this paper and results of the latter can be found in [24,29].…”

“…Therefore, the FDA guidance advises that phase 2b studies demonstrate efficacy on a histological endpoint after at least 12-18 months of treatment, given that histological change takes an extended period of time to occur using a range of doses to support phase 3 dose selection. Therefore, members of EU-PEARL are currently developing a master protocol (see Table 1) to support a phase 2b platform trial in NASH and this paper, as well as a previously published simulation study [24], describe the initiative's efforts to simulate the performance of the parameters used to make decisions on whether or not the treatment being evaluated is effective.…”

Current state-of-the-art and gaps in platform trials: 10 things you should know, insights from EU-PEARL

“…Based on the feedback of the trial stakeholders, several iterations might be needed until an optimal design addressing the various needs is found. Different aspects and risk minimisation can be of interest, e.g., the type 1 error rate on platform or sub-study level might be of regulatory interest, 18 the average and maximum sample sizes required are relevant for budgeting and portfolio optimisation.…”