Evidence map›Paper›PMID 29020256›Full record

ArticleAmerican journal of epidemiology2018

When to Censor?

Catherine R Lesko, Jessie K Edwards, Stephen R Cole, Richard D Moore, Bryan Lau

Abstract read
In one paragraph

Article in American journal of epidemiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.

0numbers the graph read from it
0cells of the map it votes in
46citing 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

46 citing papers in PubMed.

  1. Trial
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  4. Article
  5. Challenges in Malaria Diagnosis and Treatment in Kinshasa Province, Democratic Republic of the Congo.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2026
    Article
  6. Article
  7. Mortality in narcolepsy and other sleep clinic patients: a 25-year propensity-matched VA cohort study.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2026
    Article
  8. Article
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  15. 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

5 authors.

Catherine R LeskoDepartment of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland.
Jessie K EdwardsDepartment of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
Stephen R ColeDepartment of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
Richard D MooreDepartment of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland.
Bryan LauDepartment of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland.

Funding

Unsolicited R24 for the CFAR-Network of Integrated Clinical Sciences, CNICSR24AI067039 · NIAID · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI Michael S. Saag · 2006 to 2026
$99.4M
Virology, Immunology, and Microbiology CoreP30AI050410 · NIAID · UNIV OF NORTH CAROLINA CHAPEL HILL · PI DAVID M. MARGOLIS · 2001 to 2026
$76.9M
Vaccine Response and Immunotherapeutics SWGP30AI094189 · NIAID · JOHNS HOPKINS UNIVERSITY · PI Richard E. Chaisson · 2012 to 2026
$67.0M
Johns Hopkins HIV Clinical CohortU01DA036935 · NIDA · JOHNS HOPKINS UNIVERSITY · PI MOORE, RICHARD DOUGLAS · 2014 to 2024
$14.9M
University of North Carolina Womens Interagency HIV Study UNC WIHSU01AI103390 · NIAID · UNIV OF NORTH CAROLINA CHAPEL HILL · PI ADIMORA, ADAORA A · 2013 to 2018
$11.0M
3/4 Alcohol Research Consortium in HIV - Epidemiological Research Arm (ARCH-ERA)U01AA020793 · NIAAA · UNIVERSITY OF WASHINGTON · PI CRANE, HEIDI M., LAU, BRYAN · 2011 to 2020
$5.8M
Lung and HIV Analytical Data Coordinating Center (LHAD-CC)U01HL121812 · NHLBI · JOHNS HOPKINS UNIVERSITY · PI LAU, BRYAN · 2013 to 2017
$3.4M
Structural models for treatment and exposure effects in clinical HIV cohortsR01AI100654 · NIAID · UNIV OF NORTH CAROLINA CHAPEL HILL · PI COLE, STEPHEN R · 2013 to 2016
$1.6M
Comparative effectiveness of tailored HIV treatment plans and mortalityK01AI125087 · NIAID · UNIV OF NORTH CAROLINA CHAPEL HILL · PI EDWARDS, JESSIE · 2017 to 2021
$586k
NHLBI NIH HHS U01 HL121812NIAAA NIH HHS U01 AA020793NIAID NIH HHS K01 AI125087NIAID NIH HHS P30 AI050410NIAID NIH HHS P30 AI094189NIAID NIH HHS R01 AI100654NIAID NIH HHS R24 AI067039NIAID NIH HHS U01 AI103390NIDA NIH HHS U01 DA036935
6 · The paper itself

Abstract

Loss to follow-up is an endemic feature of time-to-event analyses that precludes observation of the event of interest. To our knowledge, in typical cohort studies with encounters occurring at regular or irregular intervals, there is no consensus on how to handle person-time between participants' last study encounter and the point at which they meet a definition of loss to follow-up. We demonstrate, using simulation and an example, that when the event of interest is captured outside of a study encounter (e.g., in a registry), person-time should be censored when the study-defined criterion for loss to follow-up is met (e.g., 1 year after last encounter), rather than at the last study encounter. Conversely, when the event of interest must be measured within the context of a study encounter (e.g., a biomarker value), person-time should be censored at the last study encounter. An inappropriate censoring scheme has the potential to result in substantial bias that may not be easily corrected.

Indexed as

Data Interpretation, StatisticalEpidemiologic Research DesignLost to Follow-UpBiasComputer SimulationHumansTime Factors

Identifiers

PMID29020256
PMCPMC6248498

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.