https://scholars.lib.ntu.edu.tw/handle/123456789/521731
標題: | A test for treatment effects in randomized controlled trials, harnessing the power of ultrahigh dimensional big data | 作者: | WEN-CHUNG LEE Lin J.-H. |
關鍵字: | big data; biostatistics; data mining; potential-outcome model; randomized controlled trial; sample size; sharp null | 公開日期: | 2019 | 出版社: | Lippincott Williams and Wilkins | 卷: | 98 | 期: | 43 | 來源出版物: | Medicine (United States) | 摘要: | Background: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. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074073209&doi=10.1097%2fMD.0000000000017630&partnerID=40&md5=683c4c81590aacd9c3b5bdbdb9376648 https://scholars.lib.ntu.edu.tw/handle/123456789/521731 |
ISSN: | 0025-7974 | DOI: | 10.1097/MD.0000000000017630 | SDG/關鍵字: | adult; 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 Size |
顯示於: | 流行病學與預防醫學研究所 |
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