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Joint modelling of time-to-event and multivariate longitudinal outcomes: recent developments and issues
Graeme L Hickey1, Pete Philipson2, Andrea Jorgensen3
1Department of Biostatistics, University of Liverpool, Waterhouse Building, 1-5 Brownlow Street, Liverpool, L69 3GL, UK. graeme.hickey@liverpool.ac.uk.
BMC Medical Research Methodology
|September 9, 2016
Summary
Multivariate joint modelling enhances predictions by integrating multiple longitudinal outcomes with time-to-event data. Current methods are complex, limiting their use in clinical research despite their established value.
Area of Science:
- Biostatistics
- Clinical Research Methodology
- Health Informatics
Background:
- Existing joint models often handle only single longitudinal outcomes and event times.
- Clinical studies frequently generate multiple longitudinal data points.
- Integrating diverse data sources improves predictive accuracy and clinical decision-making.
Purpose of the Study:
- To review methodologies for joint modeling of time-to-event and multivariate longitudinal data.
- To assess distributional assumptions, association structures, estimation methods, and software availability.
- To identify clinical applications and future research directions.
Main Methods:
- Systematic review of joint modeling techniques.
- Analysis of statistical assumptions and parameterizations (e.g., current value, random effects).
- Evaluation of software tools and implementation challenges.
Main Results:
- Numerous joint models have been proposed, frequently combining linear mixed models with proportional hazards models.
- Multivariate normal random effects are commonly used to capture correlations among longitudinal outcomes.
- Limited software availability hinders widespread adoption by medical researchers.
Conclusions:
- Multivariate joint modeling is valuable for personalized medicine.
- Current limitations in fitting these complex models restrict routine clinical application.
- Recommendations are provided to address research needs and facilitate broader implementation.
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