Evidence map›Paper›PMID 28930628›Full record

ArticleThe international journal of biostatistics2017

Estimation and Inference for the Mediation Proportion.

Daniel Nevo, Xiaomei Liao, Donna Spiegelman

Abstract read
In one paragraph

Article in The international journal of biostatistics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
62citing papers in PubMed, 2 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

62 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
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  14. Body height and the excess cancer risk in men.International journal of cancer · 2026
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  15. Observational
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2 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

3 authors.

Daniel Nevo.
Xiaomei Liao.
Donna Spiegelman.

Funding

Comprehensive Translational Science Analytics Tools for the Global Health AgendaDP1ES025459 · NIEHS · YALE UNIVERSITY · PI SPIEGELMAN, DONNA L · 2014 to 2018
$4.1M
New Methods for the design and evaluation of large HIV prevention interventionsR01AI112339 · NIAID · YALE UNIVERSITY · PI SPIEGELMAN, DONNA L · 2014 to 2017
$2.7M
NIAID NIH HHS R01 AI112339NIEHS NIH HHS DP1 ES025459
6 · The paper itself

Abstract

In epidemiology, public health and social science, mediation analysis is often undertaken to investigate the extent to which the effect of a risk factor on an outcome of interest is mediated by other covariates. A pivotal quantity of interest in such an analysis is the mediation proportion. A common method for estimating it, termed the "difference method", compares estimates from models with and without the hypothesized mediator. However, rigorous methodology for estimation and statistical inference for this quantity has not previously been available. We formulated the problem for the Cox model and generalized linear models, and utilize a data duplication algorithm together with a generalized estimation equations approach for estimating the mediation proportion and its variance. We further considered the assumption that the same link function hold for the marginal and conditional models, a property which we term "g-linkability". We show that our approach is valid whenever g-linkability holds, exactly or approximately, and present results from an extensive simulation study to explore finite sample properties. The methodology is illustrated by an analysis of pre-menopausal breast cancer incidence in the Nurses' Health Study. User-friendly publicly available software implementing those methods can be downloaded from the last author's website (SAS) or from CRAN (R).

Indexed as

Data Interpretation, StatisticalEpidemiologic MethodsModels, StatisticalBreast NeoplasmsFemaleHealth SurveysHumansmediation analysismediation proportionproportion of treatment effectthe difference method

Identifiers

PMID28930628
PMCPMC6014631

What OpenQuestion holds

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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.