One-stage individual participant data meta-analysis models for continuous and binary outcomes: Comparison of treatment coding options and estimation methods.
IPD
estimation methods
individual participant data
maximum likelihood
meta-analysis
treatment coding
Journal
Statistics in medicine
ISSN: 1097-0258
Titre abrégé: Stat Med
Pays: England
ID NLM: 8215016
Informations de publication
Date de publication:
30 08 2020
30 08 2020
Historique:
received:
20
02
2019
revised:
09
12
2019
accepted:
03
04
2020
pubmed:
13
5
2020
medline:
22
6
2021
entrez:
13
5
2020
Statut:
ppublish
Résumé
A one-stage individual participant data (IPD) meta-analysis synthesizes IPD from multiple studies using a general or generalized linear mixed model. This produces summary results (eg, about treatment effect) in a single step, whilst accounting for clustering of participants within studies (via a stratified study intercept, or random study intercepts) and between-study heterogeneity (via random treatment effects). We use simulation to evaluate the performance of restricted maximum likelihood (REML) and maximum likelihood (ML) estimation of one-stage IPD meta-analysis models for synthesizing randomized trials with continuous or binary outcomes. Three key findings are identified. First, for ML or REML estimation of stratified intercept or random intercepts models, a t-distribution based approach generally improves coverage of confidence intervals for the summary treatment effect, compared with a z-based approach. Second, when using ML estimation of a one-stage model with a stratified intercept, the treatment variable should be coded using "study-specific centering" (ie, 1/0 minus the study-specific proportion of participants in the treatment group), as this reduces the bias in the between-study variance estimate (compared with 1/0 and other coding options). Third, REML estimation reduces downward bias in between-study variance estimates compared with ML estimation, and does not depend on the treatment variable coding; for binary outcomes, this requires REML estimation of the pseudo-likelihood, although this may not be stable in some situations (eg, when data are sparse). Two applied examples are used to illustrate the findings.
Types de publication
Journal Article
Meta-Analysis
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
2536-2555Subventions
Organisme : Medical Research Council
ID : MC_UU_12023/21
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_UU_12023/29
Pays : United Kingdom
Informations de copyright
© 2020 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd.
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