Investigation of 2‐stage meta‐analysis methods for joint longitudinal and time‐to‐event data through simulation and real data application

Investigation of 2‐stage meta‐analysis methods for joint longitudinal and time‐to‐event... INTRODUCTIONUnivariate shared random effect joint models for longitudinal and time‐to‐event data simultaneously model a longitudinal and a time‐to‐event outcome. The model consists of a longitudinal submodel and a time‐to‐event submodel linked through an association structure, which quantifies the relationship between the 2 outcomes. A range of options exist in the literature for each submodel (including linear mixed effects models or splines for the longitudinal submodel and proportional hazards or accelerated failure time models for the time‐to‐event submodels). Various association structures have been used, including sharing just random effects between the submodels, sharing the current longitudinal trajectory (both the fixed and random effects), or sharing the first derivative or slope of the longitudinal trajectory. This investigation focuses on joint models that concern a single continuous longitudinal and a single possibly censored time‐to‐event outcome, linked using an association structure consisting of shared zero mean random effects with one common association parameter, termed proportional association.Methods for the joint analysis of longitudinal and time‐to‐event data are commonly used in analyses to account for study dropout and measurement error in time varying covariates, whilst producing less biased estimates of study parameters. Powney et al discuss the Magnetic trial, which reported a longitudinal case with http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Statistics in Medicine Wiley

Investigation of 2‐stage meta‐analysis methods for joint longitudinal and time‐to‐event data through simulation and real data application

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Publisher
Wiley
Copyright
Copyright © 2018 John Wiley & Sons, Ltd.
ISSN
0277-6715
eISSN
1097-0258
D.O.I.
10.1002/sim.7585
Publisher site
See Article on Publisher Site

Abstract

INTRODUCTIONUnivariate shared random effect joint models for longitudinal and time‐to‐event data simultaneously model a longitudinal and a time‐to‐event outcome. The model consists of a longitudinal submodel and a time‐to‐event submodel linked through an association structure, which quantifies the relationship between the 2 outcomes. A range of options exist in the literature for each submodel (including linear mixed effects models or splines for the longitudinal submodel and proportional hazards or accelerated failure time models for the time‐to‐event submodels). Various association structures have been used, including sharing just random effects between the submodels, sharing the current longitudinal trajectory (both the fixed and random effects), or sharing the first derivative or slope of the longitudinal trajectory. This investigation focuses on joint models that concern a single continuous longitudinal and a single possibly censored time‐to‐event outcome, linked using an association structure consisting of shared zero mean random effects with one common association parameter, termed proportional association.Methods for the joint analysis of longitudinal and time‐to‐event data are commonly used in analyses to account for study dropout and measurement error in time varying covariates, whilst producing less biased estimates of study parameters. Powney et al discuss the Magnetic trial, which reported a longitudinal case with

Journal

Statistics in MedicineWiley

Published: Jan 15, 2018

Keywords: ; ; ; ;

References

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