WEN-CHUNG LEELin J.-H.2020-11-192020-11-1920190025-7974https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074073209&doi=10.1097%2fMD.0000000000017630&partnerID=40&md5=683c4c81590aacd9c3b5bdbdb9376648https://scholars.lib.ntu.edu.tw/handle/123456789/521731Background: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').Methods: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.Results: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.Conclusion: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. Copyright ? 2019 the Author(s). Published by Wolters Kluwer Health, Inc.Englishbig data; biostatistics; data mining; potential-outcome model; randomized controlled trial; sample size; sharp null[SDGs]SDG3adult; article; big data; biostatistics; computer simulation; controlled study; data mining; female; human; male; randomized controlled trial; sample size; outcome assessment; procedures; randomized controlled trial (topic); sample size; statistical analysis; Big Data; Computer Simulation; Data Interpretation, Statistical; Humans; Outcome Assessment (Health Care); Randomized Controlled Trials as Topic; Sample Sizejournal article10.1097/MD.0000000000017630316518772-s2.0-85074073209