Evidence map›Paper›PMID 27090498›Full record

ArticleStatistics in medicine2016

Handling incomplete correlated continuous and binary outcomes in meta-analysis of individual participant data.

Manuel Gomes, Laura Hatfield, Sharon-Lise Normand

Open access · hybridAbstract read
In one paragraph

Article in Statistics in medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.9field-weighted citation impact, top 20% of its field
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

2 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. 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 at 2 institutions in 2 countries.

Manuel GomesFaculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, U.K.
Laura HatfieldDepartment of Health Care Policy, Harvard Medical School, Boston, 02115, MA, U.S.A.
Sharon-Lise NormandDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, 02115, MA, U.S.A.
Harvard University · USUniversity of London · GB

Funding

THE MDEPINET MEDICAL COUNTER MEASURES STUDYU01FD004493 · FDA · HARVARD MEDICAL SCHOOL · PI NORMAND, SHARON-LISE TERESA, RESNIC, FREDERIC S · 2012 to 2012
$1.6M
FDA HHS U01 FD004493Medical Research Council MR/K02177X/1
6 · The paper itself

Abstract

Meta-analysis of individual participant data (IPD) is increasingly utilised to improve the estimation of treatment effects, particularly among different participant subgroups. An important concern in IPD meta-analysis relates to partially or completely missing outcomes for some studies, a problem exacerbated when interest is on multiple discrete and continuous outcomes. When leveraging information from incomplete correlated outcomes across studies, the fully observed outcomes may provide important information about the incompleteness of the other outcomes. In this paper, we compare two models for handling incomplete continuous and binary outcomes in IPD meta-analysis: a joint hierarchical model and a sequence of full conditional mixed models. We illustrate how these approaches incorporate the correlation across the multiple outcomes and the between-study heterogeneity when addressing the missing data. Simulations characterise the performance of the methods across a range of scenarios which differ according to the proportion and type of missingness, strength of correlation between outcomes and the number of studies. The joint model provided confidence interval coverage consistently closer to nominal levels and lower mean squared error compared with the fully conditional approach across the scenarios considered. Methods are illustrated in a meta-analysis of randomised controlled trials comparing the effectiveness of implantable cardioverter-defibrillator devices alone to implantable cardioverter-defibrillator combined with cardiac resynchronisation therapy for treating patients with chronic heart failure. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Indexed as

Meta-Analysis as TopicBiometryData AccuracyDefibrillators, ImplantableHumansRandomized Controlled Trials as TopicBayesian analysisfully conditional specificationIPD meta-analysisjoint modellingmissing datamultiple imputation

Identifiers

PMID27090498
PMCPMC4982066
OpenAlexW2343906356

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.