Evidence map›Paper›PMID 36993419›Full record

ArticlemedRxiv : the preprint server for health sciences2023

Statistical Challenges when Analyzing SARS-CoV-2 RNA Measurements Below the Assay Limit of Quantification in COVID-19 Clinical Trials.

Carlee B Moser, Kara W Chew, Mark J Giganti, Jonathan Z Li, Evgenia Aga, Justin Ritz, Alexander L Greninger, Arzhang Cyrus Javan, Eric S Daar, Judith S Currier and 3 more

Open access · greenAbstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2023. 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, 2 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors at 7 institutions in 1 country.

Carlee B MoserCenter for Biostatistics in AIDS Research, Harvard T.H. Chan School of Public Health, Boston, 02115, USA.
Kara W ChewDepartment of Medicine, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, 90024, USA.
Mark J GigantiCenter for Biostatistics in AIDS Research, Harvard T.H. Chan School of Public Health, Boston, 02115, USA.
Jonathan Z LiDepartment of Medicine, Brigham and Women's Hospital, Harvard Medical School, Cambridge, 02139, USA.
Evgenia AgaCenter for Biostatistics in AIDS Research, Harvard T.H. Chan School of Public Health, Boston, 02115, USA.
Justin RitzCenter for Biostatistics in AIDS Research, Harvard T.H. Chan School of Public Health, Boston, 02115, USA.
Alexander L GreningerDepartment of Laboratory Medicine & Pathology, University of Washington, Seattle, 98195, USA.
Arzhang Cyrus JavanNational Institutes of Health, Rockville, 20852, USA Rachel Bender Ignacio, MD, MPH, Department of Medicine, University of Washington, Seattle, 98195, USA.
Eric S DaarLundquist Institute at Harbor-UCLA Medical Center, Torrance, 90502, USA David A Wohl, MD, Department of Medicine, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, 27599, USA.
Judith S CurrierDepartment of Medicine, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, 90024, USA.
Joseph J EronDepartment of Medicine, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, 27599, USA.
Davey M SmithDepartment of Medicine, University of California, San Diego, La Jolla, 92093, USA.
Michael D HughesDepartment of Biostatistics and Center for Biostatistics in AIDS Research, Harvard T.H. Chan School of Public Health, Boston, 02115, USA For the ACTIV-2/A5401 Study Team.
Cancer Research And Biostatistics · USUniversity of North Carolina at Chapel Hill · USUniversity of California, Los Angeles · USUniversity of Washington · USBrigham and Women's Hospital · USNational Institutes of Health · USUniversity of California San Diego · US

Funding

Leadership and Operations Center (LOC), AIDS Clinical Trials Group (ACTG); LOC 1/UM1AI068636 · NIAID · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Joseph J Eron, RAJESH T GANDHI · 2011 to 2026
$1073.1M
Statistical and Data Management Center (SDMC), AIDS Clinical Trials Group (ACTG)UM1AI068634 · NIAID · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI Marlene Ann Cooper, Michael David Hughes · 2011 to 2026
$246.6M
Validation, CLIA and Qualification (VQC): Enhancing the RS ratio as a tool for AIDS Clinical Trial Group (ACTG) tuberculosis trialsUM1AI106701 · NIAID · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Grace M Aldrovandi · 2014 to 2026
$116.8M
NIAID NIH HHS UM1 AI068634NIAID NIH HHS UM1 AI068636NIAID NIH HHS UM1 AI106701
6 · The paper itself

Abstract

Most clinical trials evaluating COVID-19 therapeutics include assessments of antiviral activity. In recently completed outpatient trials, changes in nasal SARS-CoV-2 RNA levels from baseline were commonly assessed using analysis of covariance (ANCOVA) or mixed models for repeated measures (MMRM) with single-imputation for results below assay lower limits of quantification (LLoQ). Analyzing changes in viral RNA levels with singly-imputed values can lead to biased estimates of treatment effects. In this paper, using an illustrative example from the ACTIV-2 trial, we highlight potential pitfalls of imputation when using ANCOVA or MMRM methods, and illustrate how these methods can be used when considering values <LLoQ as censored measurements. Best practices when analyzing quantitative viral RNA data should include details about the assay and its LLoQ, completeness summaries of viral RNA data, and outcomes among participants with baseline viral RNA ≥LLoQ, as well as those with viral RNA <LLoQ.

Identifiers

PMID36993419
PMCPMC10055451
OpenAlexW4327860881

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.