Lee Wen-Chung, Lin Jui-Hsiang
Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan.
Medicine (Baltimore). 2019 Oct;98(43):e17630. doi: 10.1097/MD.0000000000017630.
The randomized controlled trial (RCT) is the gold-standard research design in biomedicine. However, practical concerns often limit the sample size, n, the number of patients in a RCT. We aim to show that the power of a RCT can be increased by increasing p, the number of baseline covariates (sex, age, socio-demographic, genomic, and clinical profiles et al, of the patients) collected in the RCT (referred to as the 'dimension').
The conventional test for treatment effects is based on testing the 'crude null' that the outcomes of the subjects are of no difference between the two arms of a RCT. We propose a 'high-dimensional test' which is based on testing the 'sharp null' that the experimental intervention has no treatment effect whatsoever, for patients of any covariate profile.
Using computer simulations, we show that the high-dimensional test can become very powerful in detecting treatment effects for very large p, but not so for small or moderate p. Using a real dataset, we demonstrate that the P value of the high-dimensional test decreases as the number of baseline covariates increases, though it is still not significant.
In this big-data era, pushing p of a RCT to the millions, billions, or even trillions may someday become feasible. And the high-dimensional test proposed in this study can become very powerful in detecting treatment effects.
随机对照试验（RCT）是生物医学中的金标准研究设计。然而，实际问题常常限制样本量n，即RCT中的患者数量。我们旨在表明，通过增加p，即RCT中收集的基线协变量（患者的性别、年龄、社会人口统计学、基因组和临床特征等）的数量（称为“维度”），可以提高RCT的检验效能。
传统的治疗效果检验基于检验“粗略无效假设”，即RCT两组中受试者的结局无差异。我们提出一种“高维检验”，它基于检验“精确无效假设”，即对于任何协变量特征的患者，实验性干预都没有任何治疗效果。
通过计算机模拟，我们表明高维检验在p非常大时检测治疗效果的能力会变得很强，但在p较小或中等时并非如此。使用真实数据集，我们证明高维检验P值会随着基线协变量数量的增加而降低，尽管仍然不显著。
在这个大数据时代，将RCT的p值推至数百万、数十亿甚至数万亿，也许有朝一日会变得可行。并且本研究中提出的高维检验在检测治疗效果方面会变得非常强大。
