Decision rules for identifying combination therapies in open-entry, randomized controlled platform trials
Elias Laurin Meyer, Peter Mesenbrink, Cornelia Dunger-Baldauf,, Ekkehard Glimm, Yuhan Li, Franz K\"onig

TL;DR
This paper investigates how decision rules, data sharing, and assumptions influence the design and operating characteristics of open-entry platform trials comparing combination therapies to monotherapies and standard care.
Contribution
It introduces a framework for defining error rates and operating characteristics in open-entry platform trials, providing insights into optimal design choices through simulation.
Findings
Data sharing methods significantly affect trial efficiency.
Decision rule specifications impact error rates and power.
Assumptions about treatment efficacy influence trial outcomes.
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…
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