Evidence map›Paper›PMID 29286533›Full record

ArticleBiometrics2018

Regression analysis for secondary response variable in a case-cohort study.

Yinghao Pan, Jianwen Cai, Sangmi Kim, Haibo Zhou

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biometrics, 2018. 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.

No citing paper in PubMed yet.

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.

Yinghao PanDepartment of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, U.S.A.ORCID 0000-0002-4022-1815
Jianwen CaiDepartment of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, U.S.A.
Sangmi KimMedical College of Georgia, GRU Cancer Center, Augusta University, Augusta, Georgia 30912, U.S.A.
Haibo ZhouDepartment of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, U.S.A.ORCID 0000-0002-2468-0093

Funding

UNC-CH CENTER FOR ENVIRONMENTAL HEALTH &SUSCEPTIBILITYP30ES010126 · NIEHS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Stephanie Engel · 2001 to 2026
$36.3M
Statistical Methods for Cancer Clinical TrialsP01CA142538 · NCI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI KOSOROK, MICHAEL R · 2010 to 2019
$21.3M
Statistical Methods for Outcome-Dependent SamplingR01ES021900 · NIEHS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI ZHOU, HAIBO · 2013 to 2016
$1.1M
NCI NIH HHS P01 CA142538NIEHS NIH HHS P30 ES010126NIEHS NIH HHS R01 ES021900
6 · The paper itself

Abstract

Case-cohort study design has been widely used for its cost-effectiveness. In any real study, there are always other important outcomes of interest beside the failure time that the original case-cohort study is based on. How to utilize the available case-cohort data to study the relationship of a secondary outcome with the primary exposure obtained through the case-cohort study is not well studied. In this article, we propose a non-parametric estimated likelihood approach for analyzing a secondary outcome in a case-cohort study. The estimation is based on maximizing a semiparametric likelihood function that is built jointly on both time-to-failure outcome and the secondary outcome. The proposed estimator is shown to be consistent, efficient, and asymptotically normal. Finite sample performance is evaluated via simulation studies. Data from the Sister Study is analyzed to illustrate our method.

Indexed as

Cohort StudiesRegression AnalysisStatistics, NonparametricComputer SimulationHumansLikelihood FunctionsSiblingsStatistics as TopicCase-cohort designEstimated likelihoodSecondary outcomeSemiparametricValidation sample

Identifiers

PMID29286533
PMCPMC6026088

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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.