An integrative shrinkage estimator for random-effects meta-analysis of rare binary events.

Lie Li, Ou Bai, Xinlei Wang

Abstract read

In one paragraph

Article in Contemporary clinical trials communications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing 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

1 citing paper in PubMed.

The Trial Bank initiative to promote trustworthy living evidence for medication safety (PROMISE) of children: design and pilot validation.BMC medicine · 2026
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.

Lie LiDepartment of Statistical Science, Southern Methodist University, 3225 Daniel Avenue, P O Box 750332, Dallas, TX 75275-0332, USA.

Ou BaiDepartment of Statistical Science, Southern Methodist University, 3225 Daniel Avenue, P O Box 750332, Dallas, TX 75275-0332, USA.

Xinlei WangDepartment of Statistical Science, Southern Methodist University, 3225 Daniel Avenue, P O Box 750332, Dallas, TX 75275-0332, USA.

Funding

Wisdom of Crowds: Integrating Gene Set Enrichment Studies Involving RNA-Seq.R15GM113157 · NIGMS · SOUTHERN METHODIST UNIVERSITY · PI WANG, XINLEI · 2015 to 2015

$356k

NIGMS NIH HHS R15 GM113157

6 · The paper itself

Abstract

Meta-analysis has been a powerful tool for inferring the treatment effect between two experimental conditions from multiple studies of rare binary events. Recently, under a random-effects (RE) model, Bhaumik et al. developed a simple average (SA) estimator and showed that with the continuity correction factor 0.5, the SA estimator was the least biased among a set of commonly used estimators. In this paper, under various RE models that allow for treatment groups with equal and unequal variability (in either direction), we develop an integrative shrinkage (iSHRI) estimator based on the SA estimator, which aims to improve estimation efficiency in terms of mean squared error (MSE) that accounts for the bias-variance tradeoff. Through simulation, we find that iSHRI has better performance in general when compared with existing methods, in terms of bias, MSE, type I error and confidence interval coverage. Data examples of rosiglitazone meta-analysis are provided as well, where iSHRI yields competitive results.

Indexed as

BiasEstimation efficiencyLog odds ratioMean squared errorSparse binary data

Identifiers

PMID30023448

DOI10.1016/j.conctc.2018.04.004

PMCPMC6046515

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LicenceCC BY-NC-ND

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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.