Rejoinder: Improving precision and power in randomized trials for COVID-19 treatments using covariate adjustment, for binary, ordinal, and time-to-event outcomes
Biometrics
.
2021 Dec;77(4):1492-1494.
doi: 10.1111/biom.13495.
Epub 2021 May 29.
Authors
David Benkeser
1
,
Iván Díaz
2
,
Alex Luedtke
3
4
,
Jodi Segal
5
,
Daniel Scharfstein
6
,
Michael Rosenblum
7
Affiliations
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, 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.
PMID:
34050931
PMCID:
PMC8239503
DOI:
10.1111/biom.13495
No abstract available
MeSH terms
COVID-19 Drug Treatment*
Data Interpretation, Statistical
Humans
Randomized Controlled Trials as Topic
SARS-CoV-2
Grants and funding
DP2 LM013340/LM/NLM NIH HHS/United States
U01 FD005942/FD/FDA HHS/United States
DP2-LM013340/NH/NIH HHS/United States
U01FD005942/U.S. Food and Drug Administration
DP2-LM013340/NH/NIH HHS/United States