Randomisation can do Many Things – But it Cannot “Fail”-Reference-Cited by-同舟云学术

Randomisation can do Many Things – But it Cannot “Fail”

Published:2022-01-25 Issue:1 Volume:19 Page:20-23
ISSN:1740-9705
Container-title:Significance
language:en
Short-container-title:

Author:

Owora Arthur H.¹,Dawson John²,Gadbury Gary³,Mestre Luis M.⁴,Pavela Greg⁵,Mehta Tapan⁶,Vorland Colby J.⁷,Xun Pengcheng⁸,Allison David B.⁹

Affiliation:

1. Arthur H. Owora is assistant professor in the Department of Epidemiology and Biostatistics, Indiana University, School of Public Health-Bloomington

2. John Dawson is assistant professor in the Department of Nutritional Sciences, Texas Tech University, College of Human Sciences

3. Gary Gadbury is professor emeritus in the Department of Statistics, Kansas State University, College of Arts and Sciences

4. Luis M. Mestre is a research assistant and PhD student in epidemiology with a minor in data science at Indiana University, School of Public Health- Bloomington

5. Greg Pavela is associate professor in the Department of Health Behavior, The University of Alabama at Birmingham, School of Public Health

6. Tapan Mehta is associate professor and director of research in the Department of Health Services Administration, The University of Alabama at Birmingham, School of Health Professions

7. Colby J. Vorland is a post- doctoral fellow in the Department of Applied Health Science, Indiana University, School of Public Health-Bloomington

8. Pengcheng Xun is an associate director of epidemiology and outcomes research at Atara Biotherapeutics, United States

9. David B. Allison is dean, distinguished professor, and provost professor at Indiana University, School of Public Health-Bloomington

Abstract

Abstract Although randomisation has long been seen as crucial to reaching reliable insights from data, it is still falling victim to some peculiar – and troublesome – misconceptions. By Arthur H. Owora, John Dawson, Gary Gadbury, Luis M. Mestre, Greg Pavela, Tapan Mehta, Colby J. Vorland, Pengcheng Xun and David B. Allison

Publisher

Oxford University Press (OUP)

Subject

Statistics and Probability

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/1740-9713.01609

Reference20 articles.

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3. What's in a gold standard? In defence of randomised controlled trials;Backmann;Medicine, Health Care and Philosophy,2017

4. The ASA statement on p-values: Context, process, and purpose;Wasserstein;The American Statistician,2016

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