Individual participant data meta-analysis with mixed-effects transformation models.
Individual participant data
Meta-analysis
Mixed-effects model
Prognostic modeling
Regression
Time-to-event outcomes
Transformation model
Journal
Biostatistics (Oxford, England)
ISSN: 1468-4357
Titre abrégé: Biostatistics
Pays: England
ID NLM: 100897327
Informations de publication
Date de publication:
14 10 2022
14 10 2022
Historique:
received:
21
04
2021
revised:
03
11
2021
accepted:
22
11
2021
pubmed:
31
12
2021
medline:
19
10
2022
entrez:
30
12
2021
Statut:
ppublish
Résumé
One-stage meta-analysis of individual participant data (IPD) poses several statistical and computational challenges. For time-to-event outcomes, the approach requires the estimation of complicated nonlinear mixed-effects models that are flexible enough to realistically capture the most important characteristics of the IPD. We present a model class that incorporates general normally distributed random effects into linear transformation models. We discuss extensions to model between-study heterogeneity in baseline risks and covariate effects and also relax the assumption of proportional hazards. Within the proposed framework, data with arbitrary random censoring patterns can be handled. The accompanying $\textsf{R}$ package tramME utilizes the Laplace approximation and automatic differentiation to perform efficient maximum likelihood estimation and inference in mixed-effects transformation models. We compare several variants of our model to predict the survival of patients with chronic obstructive pulmonary disease using a large data set of prognostic studies. Finally, a simulation study is presented that verifies the correctness of the implementation and highlights its efficiency compared to an alternative approach.
Identifiants
pubmed: 34969073
pii: 6490207
doi: 10.1093/biostatistics/kxab045
pmc: PMC9566326
doi:
Types de publication
Journal Article
Meta-Analysis
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1083-1098Informations de copyright
© The Author 2021. Published by Oxford University Press.
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