Evidence map›Paper›PMID 34050931›Full record

ArticleBiometrics2021

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

David Benkeser, Iván Díaz, Alex Luedtke, Jodi Segal, Daniel Scharfstein, Michael Rosenblum

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biometrics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

David BenkeserDepartment of Biostatistics and Bioinformatics, Emory University, Atlanta, Georgia, USA.ORCID 0000-0002-1019-8343
Iván DíazDivision of Biostatistics, Department of Population Health Sciences, Weill Cornell Medicine, New York, New York, USA.
Alex LuedtkeDepartment of Statistics, University of Washington, Seattle, Washington, USA.
Jodi SegalDepartment of Medicine, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA.
Daniel ScharfsteinDivision of Biostatistics, Department of Population Health Sciences, University of Utah School of Medicine, Salt Lake City, Utah, USA.ORCID 0000-0001-7482-9653
Michael RosenblumDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA.ORCID 0000-0001-7411-4172

Funding

Johns Hopkins Center of Excellence in Regulatory Science and Innovation (JH-CERSI)U01FD005942 · FDA · JOHNS HOPKINS UNIVERSITY · PI ALEXANDER, G. CALEB · 2016 to 2025
$15.5M
High-Resolution Inference for Correlates of Vaccine ProtectionDP2LM013340 · NLM · UNIVERSITY OF WASHINGTON · PI LUEDTKE, ALEX · 2019 to 2019
$2.3M
FDA HHS U01 FD005942NIH HHS DP2-LM013340NLM NIH HHS DP2 LM013340U.S. Food and Drug Administration U01FD005942
6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Indexed as

COVID-19 Drug TreatmentData Interpretation, StatisticalHumansRandomized Controlled Trials as TopicSARS-CoV-2

Identifiers

PMID34050931
PMCPMC8239503

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.