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Phase-3 clinical trials provide the highest level of evidence on drug safetyand effectiveness needed for market approval by implementing large randomizedcontrolled trials (RCTs). However, 30-40% of these trials fail mainly becausesuch studies have inadequate sample sizes, stemming from the inability toobtain accurate initial estimates of average treatment effect parameters. Toremove this obstacle from the drug development cycle, we present a newalgorithm called Trend-Adaptive Design with a Synthetic-Intervention-BasedEstimator (TAD-SIE) that appropriately powers a parallel-group trial, astandard RCT design, by leveraging a state-of-the-art hypothesis testingstrategy and a novel trend-adaptive design (TAD). Specifically, TAD-SIE usesSECRETS (Subject-Efficient Clinical Randomized Controlled Trials usingSynthetic Intervention) for hypothesis testing, which simulates a cross-overtrial in order to boost power; doing so, makes it easier for a trial to reachtarget power within trial constraints (e.g., sample size limits). To estimatesample sizes, TAD-SIE implements a new TAD tailored to SECRETS given thatSECRETS violates assumptions under standard TADs. In addition, our TADovercomes the ineffectiveness of standard TADs by allowing sample sizes to beincreased across iterations without any condition while controllingsignificance level with futility stopping. On a real-world Phase-3 clinical RCT(i.e., a two-arm parallel-group superiority trial with an equal number ofsubjects per arm), TAD-SIE reaches typical target operating points of 80% or90% power and 5% significance level in contrast to baseline algorithms thatonly get at best 59% power and 4% significance level.

TAD-SIE: Sample Size Estimation for Clinical Randomized Controlled  Trials using a Trend-Adaptive Design with a Synthetic-Intervention-Based  Estimator