Evidence map›Paper›PMID 30032705›Full record

ArticleStatistical methods in medical research2019

A framework for meta-analysis of prediction model studies with binary and time-to-event outcomes.

Thomas Pa Debray, Johanna Aag Damen, Richard D Riley, Kym Snell, Johannes B Reitsma, Lotty Hooft, Gary S Collins, Karel Gm Moons

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Statistical methods in medical research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 179 papers, 120 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
179citing papers in PubMed, 120 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

179 citing papers in PubMed, 120 syntheses or guidelines pooled it.

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119 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

8 authors.

Thomas Pa Debray1 Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands.ORCID 0000-0002-1790-2719
Johanna Aag Damen1 Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands.
Richard D Riley3 Research Institute for Primary Care and Health Sciences, Keele University, Staffordshire, UK.
Kym Snell3 Research Institute for Primary Care and Health Sciences, Keele University, Staffordshire, UK.ORCID 0000-0001-9373-6591
Johannes B Reitsma1 Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands.
Lotty Hooft1 Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands.
Gary S Collins4 Centre for Statistics in Medicine, University of Oxford, Oxford, UK.ORCID 0000-0002-2772-2316
Karel Gm Moons1 Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands.

Funding

Cancer Research UK 16895Cancer Research UK 27294
6 · The paper itself

Abstract

It is widely recommended that any developed-diagnostic or prognostic-prediction model is externally validated in terms of its predictive performance measured by calibration and discrimination. When multiple validations have been performed, a systematic review followed by a formal meta-analysis helps to summarize overall performance across multiple settings, and reveals under which circumstances the model performs suboptimal (alternative poorer) and may need adjustment. We discuss how to undertake meta-analysis of the performance of prediction models with either a binary or a time-to-event outcome. We address how to deal with incomplete availability of study-specific results (performance estimates and their precision), and how to produce summary estimates of the

Indexed as

Meta-Analysis as TopicModels, StatisticalResearch DesignBayes TheoremCalibrationHumansPrognosisRisk AssessmentSystematic Reviews as Topicaggregate datacalibrationdiscriminationevidence synthesisMeta-analysispredictionprognosissystematic reviewvalidation

Identifiers

PMID30032705
PMCPMC6728752

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.