Evidence map›Paper›PMID 33316841›Full record

ArticleStatistics in medicine2021

Estimation of ascertainment bias and its effect on power in clinical trials with time-to-event outcomes.

Erich J Greene, Peter Peduzzi, James Dziura, Can Meng, Michael E Miller, Thomas G Travison, Denise Esserman

Abstract read
In one paragraph

Article in Statistics in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
–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

8 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Article
  4. Article
  5. Article
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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

7 authors.

Erich J GreeneDepartment of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USA.ORCID 0000-0002-9473-830X
Peter PeduzziDepartment of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USA.
James DziuraDepartment of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USA.
Can MengDepartment of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USA.
Michael E MillerDepartment of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA.
Thomas G TravisonMarcus Institute for Aging Research, Hebrew SeniorLife, Harvard Medical School, Boston, Massachusetts, USA.
Denise EssermanDepartment of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USA.ORCID 0000-0003-1326-9618

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
Wake Forest Claude D. Pepper OAIC - RenewalP30AG021332 · NIA · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI Denise Kathryn Houston · 2002 to 2026
$36.4M
Randomized Trial of a Multifactorial Fall Injury Prevention Strategy-SupplementU01AG048270 · NIA · BRIGHAM AND WOMEN'S HOSPITAL · PI BHASIN, SHALENDER, GILL, THOMAS MICHAEL · 2014 to 2019
$34.8M
Yale University Clinical and Translational Science Award ProgramUL1TR000142 · NCATS · YALE UNIVERSITY · PI SHERWIN, ROBERT S · 2012 to 2015
$31.8M
TUFTS--FIELDINGP30AG031679 · NIA · BRIGHAM AND WOMEN'S HOSPITAL · PI SHALENDER BHASIN · 2008 to 2026
$23.9M
NCATS NIH HHS UL1 TR000142NCATS NIH HHS UL1 TR001863NIA NIH HHS P30 AG021332NIA NIH HHS P30 AG031679NIA NIH HHS U01 AG048270
6 · The paper itself

Abstract

While the gold standard for clinical trials is to blind all parties-participants, researchers, and evaluators-to treatment assignment, this is not always a possibility. When some or all of the above individuals know the treatment assignment, this leaves the study open to the introduction of postrandomization biases. In the Strategies to Reduce Injuries and Develop Confidence in Elders (STRIDE) trial, we were presented with the potential for the unblinded clinicians administering the treatment, as well as the individuals enrolled in the study, to introduce ascertainment bias into some but not all events comprising the primary outcome. In this article, we present ways to estimate the ascertainment bias for a time-to-event outcome, and discuss its impact on the overall power of a trial vs changing of the outcome definition to a more stringent unbiased definition that restricts attention to measurements less subject to potentially differential assessment. We found that for the majority of situations, it is better to revise the definition to a more stringent definition, as was done in STRIDE, even though fewer events may be observed.

Indexed as

BiasAgedHumansascertainment biascluster-randomized trialstudy powertime-to-event data

Identifiers

PMID33316841
PMCPMC9007163

What OpenQuestion holds

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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.