Evidence map›Paper›PMID 25924725›Full record

SynthesisStatistics in medicine2015

Multivariate meta-analysis of prognostic factor studies with multiple cut-points and/or methods of measurement.

Richard D Riley, Eleni G Elia, Gemma Malin, Karla Hemming, Malcolm P Price

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

Synthesis in Statistics in medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 13 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 13 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

23 citing papers in PubMed, 13 syntheses or guidelines pooled it.

  1. Pooled it
  2. Incidence and Outcomes of COVID-19 in People With CKD: A Systematic Review and Meta-analysis.American journal of kidney diseases : the official journal of the National Kidney Foundation · 2021
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  20. Evidence synthesis in prognosis research.Diagnostic and prognostic research · 2019
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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

5 authors.

Richard D RileyResearch Institute of Primary Care and Health Sciences, Keele University, Staffordshire, ST5 5BG, U.K.
Eleni G EliaSchool of Health and Population Sciences, Public Health Building, University of Birmingham, Edgbaston Birmingham, B15 2TT, U.K.
Gemma MalinSchool of MedicineD Floor Queen's Medical Centre, University of Nottingham, Nottingham, NG7 2UH, U.K.
Karla HemmingSchool of Health and Population Sciences, Public Health Building, University of Birmingham, Edgbaston Birmingham, B15 2TT, U.K.
Malcolm P PriceSchool of Health and Population Sciences, Public Health Building, University of Birmingham, Edgbaston Birmingham, B15 2TT, U.K.

Funding

Medical Research Council G0902393Medical Research Council MR/J013595/1Medical Research Council MR/K006584/1
6 · The paper itself

Abstract

A prognostic factor is any measure that is associated with the risk of future health outcomes in those with existing disease. Often, the prognostic ability of a factor is evaluated in multiple studies. However, meta-analysis is difficult because primary studies often use different methods of measurement and/or different cut-points to dichotomise continuous factors into 'high' and 'low' groups; selective reporting is also common. We illustrate how multivariate random effects meta-analysis models can accommodate multiple prognostic effect estimates from the same study, relating to multiple cut-points and/or methods of measurement. The models account for within-study and between-study correlations, which utilises more information and reduces the impact of unreported cut-points and/or measurement methods in some studies. The applicability of the approach is improved with individual participant data and by assuming a functional relationship between prognostic effect and cut-point to reduce the number of unknown parameters. The models provide important inferential results for each cut-point and method of measurement, including the summary prognostic effect, the between-study variance and a 95% prediction interval for the prognostic effect in new populations. Two applications are presented. The first reveals that, in a multivariate meta-analysis using published results, the Apgar score is prognostic of neonatal mortality but effect sizes are smaller at most cut-points than previously thought. In the second, a multivariate meta-analysis of two methods of measurement provides weak evidence that microvessel density is prognostic of mortality in lung cancer, even when individual participant data are available so that a continuous prognostic trend is examined (rather than cut-points).

Indexed as

PrognosisApgar ScoreBiostatisticsBreast NeoplasmsFemaleHumansInfantInfant MortalityInfant, NewbornKi-67 AntigenLinear ModelsLung NeoplasmsMicrovesselsModels, BiologicalModels, StatisticalMultivariate AnalysisKi-67 Antigencut-pointsheterogeneitymultivariate meta-analysisodds ratios and hazard ratiosprognostic factors

Identifiers

PMID25924725
PMCPMC4973834

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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.