Evidence map›Paper›PMID 31206759›Full record

ArticleStatistics in medicine2019

A parametric meta-analysis.

Chang Yu, Daniel Zelterman

Open access · greenAbstract read
In one paragraph

Article in Statistics in medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
0.2field-weighted citation impact, top 40% 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

2 citing papers in PubMed, 1 synthesis or guideline pooled it, 3 citations in OpenAlex.

  1. Pooled it
  2. Review
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

2 authors at 2 institutions in 1 country.

Chang YuDepartment of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee.ORCID 0000-0002-6132-6085
Daniel ZeltermanDepartment of Biostatistics, Yale University School of Public Health, New Haven, Connecticut.
Vanderbilt University Medical Center · USYale University · US

Funding

Vanderbilt Institute for Clinical and Translational Research (VICTR) -Identifying correlates of functional immunity in SARS-CoV-2 convalescent plasmaUL1TR002243 · NCATS · VANDERBILT UNIVERSITY MEDICAL CENTER · PI Paul A. Harris, Wesley H Self · 2017 to 2026
$130.7M
Yale Pathology Tissue Services Shared ResourceP30CA016359 · NCI · YALE UNIVERSITY · PI DAVID L RIMM · 1985 to 2026
$85.0M
Yale SPORE in Lung Cancer (YSILC): The Biology and Personalized Treatment of Lung CancerP50CA196530 · NCI · YALE UNIVERSITY · PI Shuangge Ma · 2015 to 2026
$31.1M
Sex Hormones in Pulmonary Arterial HypertensionP01HL108800 · NHLBI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI LOYD, JAMES E · 2012 to 2021
$22.0M
Oncometabolites, DNA Repair, and CancerR01ES005775 · NIEHS · YALE UNIVERSITY · PI PETER M GLAZER · 1998 to 2026
$8.0M
Meeting Community Needs Across the Prevention SpectrumU48DP005023 · DP · YALE UNIVERSITY · PI DUFFANY, KATHLEEN O'CONNOR · 2014 to 2018
$3.9M
The Vanderbilt Institute for Clinical and Translational Research (VICTR)KL2TR000446 · NCATS · VANDERBILT UNIVERSITY MEDICAL CENTER · PI BERNARD, GORDON RAPHAEL · 2012 to 2016
$2.8M
Personalized Prevention of Colorectal CancerR01CA149633 · NCI · VANDERBILT UNIVERSITY · PI DAI, QI, YU, CHANG · 2010 to 2014
$2.0M
Cell-Penetrating Anti-DNA Antibody for Radiosensitization and Cancer TherapyR01CA168733 · NCI · YALE UNIVERSITY · PI GLAZER, PETER M · 2013 to 2017
$1.7M
Phase 2 Norepinephrine Transporter Blockade, Autonomic Failure IND117394 12/28/12R01FD004778 · FDA · VANDERBILT UNIVERSITY MEDICAL CENTER · PI KAUFMANN, HORACIO, SHIBAO, CYNDYA ADRIANA · 2016 to 2019
$1.6M
Bifunctional Antibodies for Melanoma TherapyR01CA177719 · NCI · YALE UNIVERSITY · PI GLAZER, PETER M · 2014 to 2016
$1.3M
Statistical Methods for Recurrent Event Data in RSV Immunoprophylaxis StudiesR21HL129020 · NHLBI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI YU, CHANG · 2015 to 2016
$250k
FDA HHS R01 FD004778NCATS NIH HHS KL2 TR000446NCCDPHP CDC HHS U48 DP005023NCI NIH HHS P30 CA016359NCI NIH HHS P50 CA196530NCI NIH HHS R01 CA149633NCI NIH HHS R01 CA168733NCI NIH HHS R01 CA177719NHLBI NIH HHS P01 HL108800NHLBI NIH HHS R21 HL129020NIEHS NIH HHS R01 ES005775NIH HHS P50-CA196530NIH HHS R41-A120546
6 · The paper itself

Abstract

In a meta-analysis, we assemble a sample of independent, nonidentically distributed p-values. The Fisher's combination procedure provides a chi-squared test of whether the p-values were sampled from the null uniform distribution. After rejecting the null uniform hypothesis, we are faced with the problem of how to combine the assembled p-values. We first derive a distribution for the p-values. The distribution is parameterized by the standardized mean difference (SMD) and the sample size. It includes the uniform as a special case. The maximum likelihood estimate (MLE) of the SMD can then be obtained from the independent, nonidentically distributed p-values. The MLE can be interpreted as a weighted average of the study-specific estimate of the effect size with a shrinkage. The method is broadly applicable to p-values obtained in the maximum likelihood framework. Simulation studies show that our method can effectively estimate the effect size with as few as 6 p-values in the meta-analyses. We also present a Bayes estimator for SMD and a method to account for publication bias. We demonstrate our methods on several meta-analyses that assess the potential benefits of citicoline for patients with memory disorders or patients recovering from ischemic stroke.

Indexed as

Likelihood FunctionsMeta-Analysis as TopicBayes TheoremComputer SimulationHumansBayesian estimatorciticolinedistribution of p-valuesstandardized mean difference

Identifiers

PMID31206759
PMCPMC6688941
OpenAlexW2952964338

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.