Decision rules for identifying combination therapies in open-entry,\n randomized controlled platform trials

Abstract

Platform trials have become increasingly popular for drug development\nprograms, attracting interest from statisticians, clinicians and regulatory\nagencies. Many statistical questions related to designing platform trials -\nsuch as the impact of decision rules, sharing of information across cohorts,\nand allocation ratios on operating characteristics and error rates - remain\nunanswered. In many platform trials, the definition of error rates is not\nstraightforward as classical error rate concepts are not applicable. For an\nopen-entry, exploratory platform trial design comparing combination therapies\nto the respective monotherapies and standard-of-care, we define a set of error\nrates and operating characteristics and then use these to compare a set of\ndesign parameters under a range of simulation assumptions. When setting up the\nsimulations, we aimed for realistic trial trajectories, such that e.g. a priori\nwe do not know the exact number of treatments that will be included over time\nin a specific simulation run as this follows a stochastic mechanism. Our\nresults indicate that the method of data sharing, exact specification of\ndecision rules and a priori assumptions regarding the treatment efficacy all\nstrongly contribute to the operating characteristics of the platform trial.\nFurthermore, different operating characteristics might be of importance to\ndifferent stakeholders. Together with the potential flexibility and complexity\nof a platform trial, which also impact the achieved operating characteristics\nvia e.g. the degree of efficiency of data sharing, this implies that utmost\ncare needs to be given to evaluation of different assumptions and design\nparameters at the design stage.\n