Evidence map›Paper›PMID 39668973›Full record

ArticleResearch methods in medicine & health sciences2025

Choice of Link Functions for Generalized Linear Mixed Models in Meta-Analyses of Proportions.

Lianne K Siegel, Milena Silva, Lifeng Lin, Yong Chen, Yu-Lun Liu, Haitao Chu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Research methods in medicine & health sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 3 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

5 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Article
  5. 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

6 authors.

Lianne K SiegelDivision of Biostatistics, University of Minnesota, Minneapolis, MN, 55455, USA.
Milena SilvaDivision of Biostatistics, University of Minnesota, Minneapolis, MN, 55455, USA.
Lifeng LinDepartment of Epidemiology and Biostatistics, University of Arizona, Tucson, AZ 85724, USA.
Yong ChenDepartment of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104.
Yu-Lun LiuDepartment of Population and Data Sciences, UT Southwestern Medical Center, Dallas, TX, 75390.
Haitao ChuStatistical Research and Data Science Center, Pfizer Inc., New York, NY.

Funding

Statistical Methods and Software for Multivariate Meta-analysisR01LM012982 · NLM · UNIVERSITY OF MINNESOTA · PI LIN, LIFENG, SIEGEL, LIANNE · 2019 to 2022
$1.3M
Training in Biostatistics for Heart, Lung and Blood Disease ResearchT32HL129956 · NHLBI · UNIVERSITY OF MINNESOTA · PI NEATON, JAMES DENNIS · 2016 to 2020
$1.1M
NHLBI NIH HHS T32 HL129956NLM NIH HHS R01 LM012982
6 · The paper itself

Abstract

Two-step approaches for synthesizing proportions in a meta-analysis require first transforming the proportions to a scale where their distribution across studies can be approximated by a normal distribution. Commonly used transformations include the log, logit, arcsine, and Freeman-Tukey double-arcsine transformations. Alternatively, a generalized linear mixed model (GLMM) can be fit directly on the data using the exact binomial likelihood. Unlike popular two-step methods, this accounts for uncertainty in the within-study variances without a normal approximation and does not require an

Indexed as

AICgeneralized linear mixed modellink functionmeta-analysisprevalenceproportion

Identifiers

PMID39668973
PMCPMC11632795

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.