Joint Modeling of Longitudinal and Time-to-Event Data
Summary
Joint modeling of longitudinal and time-to-event data unifies repeated measures and survival outcomes within a single inferential framework. By linking a mixed-effects submodel for serial biomarker or clinical measurements with a hazard submodel for event times, it accounts for measurement error, informative dropout and time-dependent covariate effects. Association structures—often based on current biomarker value or latent random effects—allow the strength and form of linkage to vary. Estimation may follow a full likelihood approach via maximum likelihood or Bayesian Markov chain Monte Carlo, or employ approximate two-stage algorithms for computational tractability. Extensions support multivariate longitudinal outcomes, competing risks, recurrent events and time-varying association parameters. The approach enhances personalised risk prediction, dynamic decision making and efficient use of all available data, with applications spanning oncology, cardiology, neurology and public health. Continued advances address high-dimensional settings, flexible association functions and robust validation tools for dynamic prediction.
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Joint Modeling of Longitudinal and Time-to-Event Data publication trend
The graph below shows the total number of articles in joint modeling of longitudinal and time-to-event data across all publications each year (not limited to Nature Index journals).
Technical terms
Longitudinal data: Repeated measurements collected on the same subjects over time to capture temporal trends.
Time-to-event data: Information on the duration until an event of interest occurs, properly accounting for censoring.
Joint model: A combined statistical framework linking longitudinal and event-time submodels through shared components to borrow strength between processes.
Shared random effects: Latent individual-level parameters that connect longitudinal trajectories and event hazards, capturing unobserved heterogeneity.
Dynamic prediction: Updated individual risk estimates for an impending event, continuously refined as new longitudinal measurements become available.
References
- Analysis of Longitudinal and Survival Data: Joint Modeling, Inference Methods, and Issues. Journal of Probability and Statistics (2011).
- joineRML: a joint model and software package for time-to-event and multivariate longitudinal outcomes. BMC Medical Research Methodology (2018).
- Bayesian joint modelling of longitudinal and time to event data: a methodological review. BMC Medical Research Methodology (2020).
- Joint models with multiple longitudinal outcomes and a time-to-event outcome: a corrected two-stage approach. Statistics and Computing (2020).
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