Evidence map›Paper›PMID 31680705›Full record

ArticleJournal of multivariate analysis2019

A semiparametric efficient estimator in case-control studies for gene-environment independent models.

Liang Liang, Yanyuan Ma, Raymond J Carroll

Abstract read
In one paragraph

Article in Journal of multivariate analysis, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Semiparametrically efficient estimation in quantile regression of secondary analysis.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2018
    Article
  4. Article
  5. Article
  6. Semiparametric Estimation in the Secondary Analysis of Case-Control Studies.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2016
    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

3 authors.

Liang LiangDepartment of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA.
Yanyuan MaDepartment of Statistics, Penn State University, University Park, PA 16802, USA.
Raymond J CarrollDepartment of Statistics, Texas A&M University, 3143 TAMU, College Station, TX 77843, USA.

Funding

Measurement Error, Nutrition, Physical Activity and CancerU01CA057030 · NCI · TEXAS A&M UNIVERSITY · PI CARROLL, RAYMOND J. · 2015 to 2019
$2.8M
NCI NIH HHS U01 CA057030
6 · The paper itself

Abstract

Case-controls studies are popular epidemiological designs for detecting gene-environment interactions in the etiology of complex diseases, where the genetic susceptibility and environmental exposures may often be reasonably assumed independent in the source population. Various papers have presented analytical methods exploiting gene-environment independence to achieve better efficiency, all of which require either a rare disease assumption or a distributional assumption on the genetic variables. We relax both assumptions. We construct a semiparametric estimator in case-control studies exploiting gene-environment independence, while the distributions of genetic susceptibility and environmental exposures are both unspecified and the disease rate is assumed unknown and is not required to be close to zero. The resulting estimator is semiparametric efficient and its superiority over prospective logistic regression, the usual analysis in case-control studies, is demonstrated in various numerical illustrations.

Indexed as

Biased samplesCase-control studyGene–environment independenceGene–environment interactionSemiparametric estimation

Identifiers

PMID31680705
PMCPMC6824552

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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.