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TAD-SIE：结合趋势自适应设计与合成干预估计的随机临床试验样本量确定AI 解读

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…

SECRETS：利用合成干预提高随机临床试验的受试者效率AI 解读

The randomized controlled trial (RCT) is the gold standard for estimating the average treatment effect (ATE) of a medical intervention but requires 100s-1000s of subjects, making it expensive and difficult to implement. While a cross-over trial can reduce sample size requirements by measuring the treatment effect per individual, it is only applicable to chronic conditions and interventions whose effects dissipate rapidly. Another approach is to replace or augment data…