Jung Sin-Ho
Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27710, USA.
J Pers Med. 2021 May 4;11(5):376. doi: 10.3390/jpm11050376.
Biomarkers play a key role in the development of personalized medicine. Cancer clinical trials with biomarker should be appropriately designed and analyzed reflecting the various factors, such as the phase of trials, the type of biomarker, the study objectives, and whether the used biomarker is already validated or not. In this paper, we demonstrate design and analysis of two phase II cancer clinical trials, one with a predictive biomarker and the other with a prognostic biomarker. A statistical testing method and its sample size calculation method are presented for each of the trials. We assume that the primary endpoint of these trials is a time to event variable, but this concept can be used for any type of endpoint with associated testing methods. The test statistics and their sample size formulas are derived using the large sample approximation based on the martingale central limit theorem. Using simulations, we find that the test statistics control the type I error rate accurately and the sample sizes calculated using the formulas maintain the statistical power specified at the design stage.
生物标志物在个性化医疗的发展中起着关键作用。涉及生物标志物的癌症临床试验应进行适当设计和分析，以反映各种因素，如试验阶段、生物标志物类型、研究目标，以及所使用的生物标志物是否已经过验证。在本文中，我们展示了两项II期癌症临床试验的设计和分析，一项使用预测性生物标志物，另一项使用预后性生物标志物。针对每项试验，我们给出了一种统计检验方法及其样本量计算方法。我们假设这些试验的主要终点是一个事件发生时间变量，但这个概念可用于任何类型的终点及相关检验方法。基于鞅中心极限定理，利用大样本近似推导出检验统计量及其样本量公式。通过模拟，我们发现检验统计量能准确控制I型错误率，并且使用公式计算出的样本量能保持设计阶段指定的统计效能。
