Evidence map›Paper›PMID 26484424›Full record

ArticleEpidemiology (Cambridge, Mass.)2016

Selection Bias Due to Loss to Follow Up in Cohort Studies.

Chanelle J Howe, Stephen R Cole, Bryan Lau, Sonia Napravnik, Joseph J Eron

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Epidemiology (Cambridge, Mass.), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 290 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
290citing papers in PubMed, 6 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

290 citing papers in PubMed, 6 syntheses or guidelines pooled it.

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  20. The Aarhus bereavement study (TABstudy).European journal of epidemiology · 2026
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230 more citing papers are in PubMed but not listed here.

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.

Chanelle J HoweFrom the aDepartment of Epidemiology, Center for Population Health and Clinical Epidemiology, Brown University School of Public Health, Providence, RI; bDepartment of Epidemiology, University of North Carolina Gillings School of Global Public Health, Chapel Hill, NC; cDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; and dDivision of Infectious Diseases, Department of Medicine, University of North Carolina School of Medicine, Chapel Hill, NC.
Stephen R Cole
Bryan Lau
Sonia Napravnik
Joseph J Eron

Funding

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
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
NHLBI NIH HHS U01 HL121812NIAAA NIH HHS U01 AA020793NIAID NIH HHS P30 AI050410NIAID NIH HHS P30 AI094189NIAID NIH HHS P30 AI50410
6 · The paper itself

Abstract

Selection bias due to loss to follow up represents a threat to the internal validity of estimates derived from cohort studies. Over the past 15 years, stratification-based techniques as well as methods such as inverse probability-of-censoring weighted estimation have been more prominently discussed and offered as a means to correct for selection bias. However, unlike correcting for confounding bias using inverse weighting, uptake of inverse probability-of-censoring weighted estimation as well as competing methods has been limited in the applied epidemiologic literature. To motivate greater use of inverse probability-of-censoring weighted estimation and competing methods, we use causal diagrams to describe the sources of selection bias in cohort studies employing a time-to-event framework when the quantity of interest is an absolute measure (e.g., absolute risk, survival function) or relative effect measure (e.g., risk difference, risk ratio). We highlight that whether a given estimate obtained from standard methods is potentially subject to selection bias depends on the causal diagram and the measure. We first broadly describe inverse probability-of-censoring weighted estimation and then give a simple example to demonstrate in detail how inverse probability-of-censoring weighted estimation mitigates selection bias and describe challenges to estimation. We then modify complex, real-world data from the University of North Carolina Center for AIDS Research HIV clinical cohort study and estimate the absolute and relative change in the occurrence of death with and without inverse probability-of-censoring weighted correction using the modified University of North Carolina data. We provide SAS code to aid with implementation of inverse probability-of-censoring weighted techniques.

Indexed as

Cohort StudiesData Interpretation, StatisticalLost to Follow-UpSelection BiasAdultFemaleFollow-Up StudiesHIV InfectionsHumansMaleNorth CarolinaProbability

Identifiers

PMID26484424
PMCPMC5008911

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.