To randomize, or not to randomize, that is the question: using data from prior clinical trials to guide future designs.

Abstract

BACKGROUND\nUnderstanding the value of randomization is critical in designing clinical trials. Here, we introduce a simple and interpretable quantitative method to compare randomized designs versus single arm designs using indication-specific parameters derived from the literature. We demonstrate the approach through application to phase II trials in newly diagnosed glioblastoma (ndGBM).\n\n\nMETHODS\nWe abstracted data from prior ndGBM trials and derived relevant parameters to compare phase II RCT and single arm designs within a quantitative framework. Parameters included in our model were (i) the variability of the primary endpoint distributions across studies, (ii) potential for incorrectly specifying the single arm trial s benchmark, and (iii) the hypothesized effect size. Strengths and weaknesses of RCT and single arm designs were quantified by various metrics, including power and false positive errors rates.\n\n\nRESULTS\nWe applied our method to show that RCTs should be preferred to single arm trials for evaluating overall survival in ndGBM patients based on parameters estimated from prior trials. More generally, for a given effect size, the utility of randomization compared to single arm designs is highly dependent on (i) inter-study variability of the outcome distributions and (ii) on potential errors in selecting standard of care efficacy estimates for single arm studies.\n\n\nCONCLUSIONS\nA quantitative framework using historical data is useful in understanding the utility of randomization in designing prospective trials. For typical phase II ndGBM trials using OS as the primary endpoint, randomization should be preferred over single arm designs.