Evidence mapPaperPMID 31330089Full record

ArticleResearch synthesis methods2020

Shared parameter model for competing risks and different data summaries in meta-analysis: Implications for common and rare outcomes.

Howard Thom, José A López-López, Nicky J Welton

Abstract read
In one paragraph

Article in Research synthesis methods, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 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

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. A Review of Methods for Research Synthesis.Statistics in medicine · 2025
    Article
  4. Article
  5. Multilevel and Quasi Monte Carlo Methods for the Calculation of the Expected Value of Partial Perfect Information.Medical decision making : an international journal of the Society for Medical Decision Making · 2022
    Article
  6. Article
  7. 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

3 authors.

Howard ThomBristol Medical School: Population Health Sciences, University of Bristol, Bristol, UK.ORCID https://orcid.org/0000-0001-8576-5552
José A López-LópezBristol Medical School: Population Health Sciences, University of Bristol, Bristol, UK.ORCID https://orcid.org/0000-0002-9655-3616
Nicky J WeltonBristol Medical School: Population Health Sciences, University of Bristol, Bristol, UK.ORCID https://orcid.org/0000-0003-2198-3205

Funding

Department of Health 11/92/17Department of Health 14/141/01Medical Research Council Hubs for Trials Methodology Research CollaborationMedical Research Council MR/K025643/1Medical Research Council MR/M005615/1Medical Research Council MR/P015298/1
6 · The paper itself

Abstract

This paper considers the problem in aggregate data meta-analysis of studies reporting multiple competing binary outcomes and of studies using different summary formats for those outcomes. For example, some may report numbers of patients with at least one of each outcome while others may report the total number of such outcomes. We develop a shared parameter model on hazard ratio scale accounting for different data summaries and competing risks. We adapt theoretical arguments from the literature to demonstrate that the models are equivalent if events are rare. We use constructed data examples and a simulation study to find an event rate threshold of approximately 0.2 above which competing risks and different data summaries may bias results if no adjustments are made. Below this threshold, simpler models may be sufficient. We recommend analysts to consider the absolute event rates and only use a simple model ignoring data types and competing risks if all of underlying events are rare (below our threshold of approximately 0.2). If one or more of the absolute event rates approaches or exceeds our informal threshold, it may be necessary to account for data types and competing risks through a shared parameter model in order to avoid biased estimates.

Indexed as

Meta-Analysis as TopicRandomized Controlled Trials as TopicTreatment OutcomeAdministration, OralAlgorithmsAnticoagulantsAsymptomatic InfectionsAtrial FibrillationComputer SimulationDabigatranHemorrhageHumansLikelihood FunctionsMotivationMyocardial InfarctionOdds RatioAnticoagulantsDabigatranWarfarincompeting risksdifferent data summariesmeta-analysisnetwork meta-analysisrare eventsshared parameter models

Identifiers

PMID31330089
PMCPMC7003901

What Socratic 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 Socratic graph.