  Phase-3 clinical trials provide the highest level of evidence on drug safety
and effectiveness needed for market approval by implementing large randomized
controlled trials (RCTs). However, 30-40% of these trials fail mainly because
such studies have inadequate sample sizes, stemming from the inability to
obtain accurate initial estimates of average treatment effect parameters. To
remove this obstacle from the drug development cycle, we present a new
algorithm called Trend-Adaptive Design with a Synthetic-Intervention-Based
Estimator (TAD-SIE) that appropriately powers a parallel-group trial, a
standard RCT design, by leveraging a state-of-the-art hypothesis testing
strategy and a novel trend-adaptive design (TAD). Specifically, TAD-SIE uses
SECRETS (Subject-Efficient Clinical Randomized Controlled Trials using
Synthetic Intervention) for hypothesis testing, which simulates a cross-over
trial in order to boost power; doing so, makes it easier for a trial to reach
target power within trial constraints (e.g., sample size limits). To estimate
sample sizes, TAD-SIE implements a new TAD tailored to SECRETS given that
SECRETS violates assumptions under standard TADs. In addition, our TAD
overcomes the ineffectiveness of standard TADs by allowing sample sizes to be
increased across iterations without any condition while controlling
significance 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 of
subjects per arm), TAD-SIE reaches typical target operating points of 80% or
90% power and 5% significance level in contrast to baseline algorithms that
only get at best 59% power and 4% significance level.
