Evidence map›Paper›PMID 28238045›Full record

ArticleLifetime data analysis2018

Estimation of the cumulative incidence function under multiple dependent and independent censoring mechanisms.

Judith J Lok, Shu Yang, Brian Sharkey, Michael D Hughes

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Lifetime data analysis, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
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

4 authors.

Judith J LokDepartment of Biostatistics, Harvard School of Public Health, 655 Huntington Avenue, Boston, MA, 02115, USA. jlok@hsph.harvard.edu.ORCID 0000-0001-7526-2572
Shu YangDepartment of Statistics, North Carolina State University, Raleigh, NC, USA.
Brian SharkeyIncyte, Wilmington, DE, USA.
Michael D HughesDepartment of Biostatistics, Harvard School of Public Health, 655 Huntington Avenue, Boston, MA, 02115, USA.

Funding

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
Statistical and Data Management Center for the AIDS Clinical Trials GroupU01AI068634 · NIAID · HARVARD SCHOOL OF PUBLIC HEALTH · PI HUGHES, MICHAEL DAVID · 2006 to 2010
$71.9M
BIOSTATISTICS/EPIDEMIOLOGY TRAINING GRANTS IN AIDST32AI007358 · NIAID · HARVARD UNIVERSITY (SCH OF PUBLIC HLTH) · PI Michael David Hughes · 1989 to 2026
$12.4M
STATISTICAL METHODS IN AIDS RESEARCHR37AI024643 · NIAID · HARVARD UNIVERSITY (SCH OF PUBLIC HLTH) · PI HUGHES, MICHAEL DAVID · 2001 to 2010
$2.8M
STATISTICAL METHODS IN AIDS RESEARCHR01AI024643 · NIAID · HARVARD UNIVERSITY (MEDICAL SCHOOL) · PI HUGHES, MICHAEL DAVID · 1987 to 2014
$1.7M
NIAID NIH HHS R01 AI024643NIAID NIH HHS R37 AI024643NIAID NIH HHS T32 AI007358NIAID NIH HHS U01 AI068634NIAID NIH HHS UM1 AI068634
6 · The paper itself

Abstract

Competing risks occur in a time-to-event analysis in which a patient can experience one of several types of events. Traditional methods for handling competing risks data presuppose one censoring process, which is assumed to be independent. In a controlled clinical trial, censoring can occur for several reasons: some independent, others dependent. We propose an estimator of the cumulative incidence function in the presence of both independent and dependent censoring mechanisms. We rely on semi-parametric theory to derive an augmented inverse probability of censoring weighted (AIPCW) estimator. We demonstrate the efficiency gained when using the AIPCW estimator compared to a non-augmented estimator via simulations. We then apply our method to evaluate the safety and efficacy of three anti-HIV regimens in a randomized trial conducted by the AIDS Clinical Trial Group, ACTG A5095.

Indexed as

BiasIncidenceAlgorithmsDrug TherapyHIV InfectionsModels, StatisticalMulticenter Studies as TopicRandomized Controlled Trials as TopicSafetyCompeting risksCumulative incidence functionDependent censoringInverse probability weighting

Identifiers

PMID28238045
PMCPMC5572121

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Registered trials

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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.