Abstract：
Clinical trials with continuous primary endpoints typically measure outcomes at baseline, at a fixed timepoint (denoted Tmin), and at intermediate timepoints. The analysis is commonly performed using the mixed model repeated measures method. It is sometimes expected that the effect size will be larger with follow-up longer than Tmin. But extending the follow-up for all patients delays trial completion. We propose an alternative trial design and analysis method that potentially increases statistical power without extending the trial duration or increasing the sample size. We propose following the last enrolled patient until Tmin, with earlier enrollees having variable follow-up durations up to a maximum of Tmax. The sample size at Tmax will be smaller than at Tmin, and due to staggered enrollment, data missing at Tmax will be missing completely at random. For analysis, we propose an alpha-adjusted procedure based on the smaller of the p values at Tmin and Tmax, termed minP $$ minP $$ . This approach can provide the highest power when the powers at Tmin and Tmax are similar. If the power at Tmin and Tmax differ significantly, the power of minP $$ minP $$ is modestly reduced compared with the larger of the two powers. Rare disease trials, due to the limited size of the patient population, may benefit the most with this design.
摘要：
具有连续主要终点的临床试验通常在基线测量结果，在固定时间点(表示为Tmin)，在中间时间点。通常使用混合模型重复度量方法进行分析。有时可以预期，随着随访时间的延长，效果大小将比Tmin更大。但是延长对所有患者的随访会延迟试验完成。我们提出了一种替代的试验设计和分析方法，该方法可能会增加统计功效，而不会延长试验持续时间或增加样本量。我们建议跟踪最后一个登记的患者，直到Tmin，较早的参与者具有可变的随访持续时间，最高可达Tmax。Tmax时的样本量将小于Tmin时的样本量，由于入学交错，在Tmax丢失的数据将完全随机丢失。为了进行分析，我们基于Tmin和Tmax处的p值中较小的一个，提出了一个alpha调整过程，称为minP$$minP$$。当Tmin和Tmax处的功率相似时,该方法可以提供最高功率。如果Tmin和Tmax处的功率显著不同，与两个幂中的较大者相比，minP$$的幂略有降低。罕见疾病试验，由于患者人数有限，可能会受益于这个设计。
