Evidence map›Paper›PMID 29208048›Full record

SynthesisSystematic reviews2017

Quantifying heterogeneity in individual participant data meta-analysis with binary outcomes.

Bo Chen, Andrea Benedetti

Abstract readMeta-AnalysisReview
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Systematic reviews, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 82 papers, 47 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
82citing papers in PubMed, 47 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

82 citing papers in PubMed, 47 syntheses or guidelines pooled it.

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22 more citing papers are in PubMed but not listed here.

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.

Bo ChenDepartment of Epidemiology, Biostatistics and Occupational Health, McGill University, Purvis Hall, 1020 Pine Avenue West, Montreal, Canada.
Andrea BenedettiDepartment of Epidemiology, Biostatistics and Occupational Health, McGill University, Purvis Hall, 1020 Pine Avenue West, Montreal, Canada. andrea.benedetti@mcgill.ca.

Funding

CIHR
6 · The paper itself

Abstract

backgroundIn meta-analyses (MA), effect estimates that are pooled together will often be heterogeneous. Determining how substantial heterogeneity is is an important aspect of MA.

methodWe consider how best to quantify heterogeneity in the context of individual participant data meta-analysis (IPD-MA) of binary data. Both two- and one-stage approaches are evaluated via simulation study. We consider conventional I

resultsResults show that when there is no effect modification, the estimated I

conclusionsThe simulation-based I

Indexed as

Models, StatisticalHumansStatistics as TopicHeterogeneityI 2Individual participant data meta-analysis (IPD-MA)Two-stage and one-stage approaches

Identifiers

PMID29208048
PMCPMC5718085

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.