Improving precision and power in randomized trials for COVID‐19 treatments using covariate adjustment, for binary, ordinal, and time‐to‐event outcomes-Reference-Cited by-同舟云学术

Improving precision and power in randomized trials for COVID‐19 treatments using covariate adjustment, for binary, ordinal, and time‐to‐event outcomes

Published:2020-10-11 Issue:4 Volume:77 Page:1467-1481
ISSN:0006-341X
Container-title:Biometrics
language:en
Short-container-title:Biometrics

Author:

Benkeser David¹^ORCID,Díaz Iván²^ORCID,Luedtke Alex³⁴,Segal Jodi⁵,Scharfstein Daniel⁶^ORCID,Rosenblum Michael⁷^ORCID

Affiliation:

1. Department of Biostatistics and Bioinformatics Emory University Atlanta Georgia USA

2. Division of Biostatistics Department of Population Health Sciences Weill Cornell Medicine New York New York USA

3. Department of Statistics University of Washington Seattle Washington USA

4. Vaccine and Infectious Disease Division Fred Hutchinson Cancer Research Center, University of Washington Seattle Washington USA

5. Department of Medicine School of Medicine Johns Hopkins University Baltimore Maryland USA

6. Division of Biostatistics Department of Population Health Sciences University of Utah School of Medicine Salt Lake City Utah USA

7. Department of Biostatistics Johns Hopkins Bloomberg School of Public Health Johns Hopkins University Baltimore Maryland USA

Funder

National Institutes of Health

U.S. Food and Drug Administration

Publisher

Wiley

Subject

Applied Mathematics,General Agricultural and Biological Sciences,General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,General Medicine,Statistics and Probability

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/biom.13377

Reference50 articles.

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4. Benkeser D. Diaz I. Luedtke A. Segal J. Scharfstein D.andRosenblum M.(2020)Improving precision and power in randomized trials for COVID‐19 treatments using covariate adjustment for ordinal or time‐to‐event outcomes. Available at:https://www.medrxiv.org/content/10.1101/2020.04.19.20069922v1?versioned=true. Accessed April 23 2020.

5. Estimating and Testing Vaccine Sieve Effects Using Machine Learning

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