Evidence map›Paper›PMID 32046631›Full record

ArticleBMC genomics2020

A novel estimator of between-study variance in random-effects models.

Nan Wang, Jun Zhang, Li Xu, Jing Qi, Beibei Liu, Yiyang Tang, Yinan Jiang, Liang Cheng, Qinghua Jiang, Xunbo Yin and 1 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC genomics, 2020. 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
0.3field-weighted citation impact, top 47% 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, 5 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

11 authors at 6 institutions in 1 country.

Nan WangSchool of Mathematics, Harbin Institute of Technology, Harbin, Heilongjiang, China.
Jun ZhangRehabilitation department, Heilongjiang Province Land Reclamation Headquarters General Hospital, Harbin, Heilongjiang, China.
Li XuCollege of Computer Science and Technology, Harbin Engineering University, Harbin, Heilongjiang, China.
Jing QiSchool of Mathematics, Harbin Institute of Technology, Harbin, Heilongjiang, China.
Beibei LiuSchool of Mathematics, Harbin Institute of Technology, Harbin, Heilongjiang, China.
Yiyang TangSchool of Mathematics, Heilongjiang University, Harbin, Heilongjiang, China.
Yinan JiangHeilongjiang Province Hospital of Chinese Medicine, Harbin, Heilongjiang, China.
Liang ChengCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.
Qinghua JiangSchool of Life Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, China.
Xunbo YinSchool of Mathematics, Harbin Institute of Technology, Harbin, Heilongjiang, China.
Shuilin JinSchool of Mathematics, Harbin Institute of Technology, Harbin, Heilongjiang, China. jinsl@hit.edu.cn.
Harbin Institute of Technology · CNHarbin Engineering University · CNHarbin Medical University · CNHeilongjiang Academy of Land Reclamation Sciences · CNHeilongjiang Provincial Hospital · CNHeilongjiang University · CN

Funding

Impact of Prenatal Alcohol Exposure on Receptor Targeting and FunctioningR03AA020101 · NIAAA · UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR · PI CALDWELL, KEVIN K. · 2010 to 2011
$148k
NIAAA NIH HHS R03 AA020101
6 · The paper itself

Abstract

backgroundWith the rapid development of high-throughput sequencing technologies, many datasets on the same biological subject are generated. A meta-analysis is an approach that combines results from different studies on the same topic. The random-effects model in a meta-analysis enables the modeling of differences between studies by incorporating the between-study variance.

resultsThis paper proposes a moments estimator of the between-study variance that represents the across-study variation. A new random-effects method (DSLD2), which involves two-step estimation starting with the DSL estimate and the [Formula: see text] in the second step, is presented. The DSLD2 method is compared with 6 other meta-analysis methods based on effect sizes across 8 aspects under three hypothesis settings. The results show that DSLD2 is a suitable method for identifying differentially expressed genes under the first hypothesis. The DSLD2 method is also applied to Alzheimer's microarray datasets. The differentially expressed genes detected by the DSLD2 method are significantly enriched in neurological diseases.

conclusionsThe results from both simulationes and an application show that DSLD2 is a suitable method for detecting differentially expressed genes under the first hypothesis.

Indexed as

Alzheimer DiseaseData Interpretation, StatisticalGene Expression ProfilingHumansLikelihood FunctionsMeta-Analysis as TopicModels, StatisticalMonte Carlo MethodROC CurveBetween-study varianceDifferentially expressed genesMeta-analysisRandom-effects model

Identifiers

PMID32046631
PMCPMC7014785
OpenAlexW3011398360

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.