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A test for treatment effects in randomized controlled trials, harnessing the power of ultrahigh dimensional big data

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 covariat...

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Autores principales: Lee, Wen-Chung, Lin, Jui-Hsiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Wolters Kluwer Health 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6824789/
https://www.ncbi.nlm.nih.gov/pubmed/31651877
http://dx.doi.org/10.1097/MD.0000000000017630
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author Lee, Wen-Chung
Lin, Jui-Hsiang
author_facet Lee, Wen-Chung
Lin, Jui-Hsiang
author_sort Lee, Wen-Chung
collection PubMed
description 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.
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spelling pubmed-68247892019-11-19 A test for treatment effects in randomized controlled trials, harnessing the power of ultrahigh dimensional big data Lee, Wen-Chung Lin, Jui-Hsiang Medicine (Baltimore) 3700 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. Wolters Kluwer Health 2019-10-25 /pmc/articles/PMC6824789/ /pubmed/31651877 http://dx.doi.org/10.1097/MD.0000000000017630 Text en Copyright © 2019 the Author(s). Published by Wolters Kluwer Health, Inc. http://creativecommons.org/licenses/by-nc/4.0 This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc/4.0
spellingShingle 3700
Lee, Wen-Chung
Lin, Jui-Hsiang
A test for treatment effects in randomized controlled trials, harnessing the power of ultrahigh dimensional big data
title A test for treatment effects in randomized controlled trials, harnessing the power of ultrahigh dimensional big data
title_full A test for treatment effects in randomized controlled trials, harnessing the power of ultrahigh dimensional big data
title_fullStr A test for treatment effects in randomized controlled trials, harnessing the power of ultrahigh dimensional big data
title_full_unstemmed A test for treatment effects in randomized controlled trials, harnessing the power of ultrahigh dimensional big data
title_short A test for treatment effects in randomized controlled trials, harnessing the power of ultrahigh dimensional big data
title_sort test for treatment effects in randomized controlled trials, harnessing the power of ultrahigh dimensional big data
topic 3700
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6824789/
https://www.ncbi.nlm.nih.gov/pubmed/31651877
http://dx.doi.org/10.1097/MD.0000000000017630
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