Evidence map›Paper›PMID 34720706›Full record

ArticleSoft computing2021

Xin Liu, RongXian Yue, Zizhao Zhang, Weng Kee Wong

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Soft computing, 2021. 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.

  1. 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

4 authors.

Xin LiuCollege of Science, Donghua University, Shanghai, 201600 China.
RongXian YueDepartment of Mathematics, Shanghai Normal University, Shanghai, 200234 China.
Zizhao ZhangDepartment of Biostatistics, University of California at Los Angeles, Los Angeles, CA 90095-1772 USA.
Weng Kee WongDepartment of Biostatistics, University of California at Los Angeles, Los Angeles, CA 90095-1772 USA.

Funding

Designing Efficient Designs under Model Uncertainty for Biological StudiesR01GM107639 · NIGMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI WONG, WENG K · 2014 to 2017
$1.0M
NIGMS NIH HHS R01 GM107639
6 · The paper itself

Abstract

Hierarchical linear models are widely used in many research disciplines and estimation issues for such models are generally well addressed. Design issues are relatively much less discussed for hierarchical linear models but there is an increasing interest as these models grow in popularity. This paper discusses the

Indexed as

Approximate designLocally D-optimal designPoisson regression modelPredictionRandom-effects model

Identifiers

PMID34720706
PMCPMC8550460

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.