Evidence map›Paper›PMID 30859548›Full record

ArticleBiometrics2019

High dimensional mediation analysis with latent variables.

Andriy Derkach, Ruth M Pfeiffer, Ting-Huei Chen, Joshua N Sampson

Open access · greenAbstract read
In one paragraph

Article in Biometrics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed
5.1field-weighted citation impact, top 4% of its field
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

28 citing papers in PubMed, 49 citations in OpenAlex.

  1. Article
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  4. Mediation analysis with graph mediator.Biostatistics (Oxford, England) · 2025
    Article
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  7. Causal Mediation Analysis for Integrating Exposure, Genomic, and Phenotype Data.Annual review of statistics and its application · 2025
    Article
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  18. The role of epigenetics in multi-generational transmission of asthma: An NIAID workshop report-based narrative review.Clinical and experimental allergy : journal of the British Society for Allergy and Clinical Immunology · 2022
    Review
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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

4 authors at 2 institutions in 2 countries.

Andriy DerkachDivision of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland.ORCID 0000-0003-2178-8493
Ruth M PfeifferDivision of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland.ORCID 0000-0001-7791-2698
Ting-Huei ChenDepartment of Mathematics and Statistics, Laval University, Quebec City, Canada.ORCID 0000-0002-7731-2374
Joshua N SampsonDivision of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland.ORCID 0000-0003-2875-3365
National Institutes of Health · USUniversité Laval · CA

Funding

Methods for Epidemiology StudiesZIACP010181 · NCI · DIVISION OF CANCER EPIDEMIOLOGY AND GENETICS · PI ALBERT, PAUL · 2009 to 2025
$34.8M
Methods for Epidemiology StudiesZ01CP010181 · NCI · DIVISION OF CANCER EPIDEMIOLOGY AND GENETICS · PI CHATTERJEE, NILANJAN · 2003 to 2008
$4.8M
Intramural NIH HHS Z01 CP010181
6 · The paper itself

Abstract

We propose a model for high dimensional mediation analysis that includes latent variables. We describe our model in the context of an epidemiologic study for incident breast cancer with one exposure and a large number of biomarkers (i.e., potential mediators). We assume that the exposure directly influences a group of latent, or unmeasured, factors which are associated with both the outcome and a subset of the biomarkers. The biomarkers associated with the latent factors linking the exposure to the outcome are considered "mediators." We derive the likelihood for this model and develop an expectation-maximization algorithm to maximize an L1-penalized version of this likelihood to limit the number of factors and associated biomarkers. We show that the resulting estimates are consistent and that the estimates of the nonzero parameters have an asymptotically normal distribution. In simulations, procedures based on this new model can have significantly higher power for detecting the mediating biomarkers compared with the simpler approaches. We apply our method to a study that evaluates the relationship between body mass index, 481 metabolic measurements, and estrogen-receptor positive breast cancer.

Indexed as

AlgorithmsLikelihood FunctionsModels, TheoreticalAnimalsBiomarkersBreast NeoplasmsComputer SimulationFemaleHumansBiomarkersdirect effectfactor analysismediation analysisoracle propertypenalized likelihood

Identifiers

PMID30859548
PMCPMC8811931
OpenAlexW2920925479

What OpenQuestion holds

Textmetadata
LicenceTDM
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.