Evidence map›Paper›PMID 34217371›Full record

ArticleMilitary Medical Research2021

Meta-analysis with zero-event studies: a comparative study with application to COVID-19 data.

Jia-Jin Wei, En-Xuan Lin, Jian-Dong Shi, Ke Yang, Zong-Liang Hu, Xian-Tao Zeng, Tie-Jun Tong

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Article in Military Medical Research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 10 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 10 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

20 citing papers in PubMed, 10 syntheses or guidelines pooled it.

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  11. Impact of branch arteries on efficacy of endoluminal flow diverters: Insights from posterior communicating artery aneurysms.Interventional neuroradiology : journal of peritherapeutic neuroradiology, surgical procedures and related neurosciences · 2026
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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

7 authors.

Jia-Jin WeiDepartment of Mathematics, Hong Kong Baptist University, Hong Kong, China.
En-Xuan LinShenzhen Research Institute of Big Data, Shenzhen, China.
Jian-Dong ShiDepartment of Mathematics, Hong Kong Baptist University, Hong Kong, China.
Ke YangDepartment of Mathematics, Hong Kong Baptist University, Hong Kong, China.
Zong-Liang HuCollege of Mathematics and Statistics, Shenzhen University, Shenzhen, China.
Xian-Tao ZengCenter for Evidence-Based and Translational Medicine, Zhongnan Hospital of Wuhan University, Wuhan, China.
Tie-Jun TongDepartment of Mathematics, Hong Kong Baptist University, Hong Kong, China. tongt@hkbu.edu.hk.ORCID 0000-0003-0947-3990

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMeta-analysis is a statistical method to synthesize evidence from a number of independent studies, including those from clinical studies with binary outcomes. In practice, when there are zero events in one or both groups, it may cause statistical problems in the subsequent analysis.

methodsIn this paper, by considering the relative risk as the effect size, we conduct a comparative study that consists of four continuity correction methods and another state-of-the-art method without the continuity correction, namely the generalized linear mixed models (GLMMs). To further advance the literature, we also introduce a new method of the continuity correction for estimating the relative risk.

resultsFrom the simulation studies, the new method performs well in terms of mean squared error when there are few studies. In contrast, the generalized linear mixed model performs the best when the number of studies is large. In addition, by reanalyzing recent coronavirus disease 2019 (COVID-19) data, it is evident that the double-zero-event studies impact the estimate of the mean effect size.

conclusionsWe recommend the new method to handle the zero-event studies when there are few studies in a meta-analysis, or instead use the GLMM when the number of studies is large. The double-zero-event studies may be informative, and so we suggest not excluding them.

Indexed as

COVID-19Data AnalysisMeta-Analysis as TopicHumansLinear ModelsResearch DesignContinuity correctionCoronavirus disease 2019 dataMeta-analysisRelative riskZero-event studies

Identifiers

PMID34217371
PMCPMC8254431

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Registered trials

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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.