ArticleStatistics in medicine2021
Backward joint model and dynamic prediction of survival with multivariate longitudinal data.
Article in Statistics in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed.
- Backward joint model for the joint dynamic prediction of time-to-event and longitudinal data: basic formulation and new developments.Lifetime data analysis · 2026Article
- Dynamic and concordance-assisted learning for risk stratification with application to Alzheimer's disease.Biostatistics (Oxford, England) · 2025Article
- A different perspective on studying stroke predictors: joint models for longitudinal and time-to-event data in a type 2 diabetes mellitus cohort.Cardiovascular diabetology · 2025Article
- Assessing biomarker trajectories for mortality risk in peritoneal dialysis: A focus on multivariate joint modeling.PloS one · 2025Article
- A comparison of two approaches to dynamic prediction: Joint modeling and landmark modeling.Statistics in medicine · 2023Article
- Longitudinal Data to Enhance Dynamic Stroke Risk Prediction.Healthcare (Basel, Switzerland) · 2022Article
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Authors and funding
2 authors.
Funding
Abstract
An important approach to dynamic prediction of time-to-event outcomes using longitudinal data is based on modeling the joint distribution of longitudinal and time-to-event data. The widely used joint model for this purpose is the shared random effect model. Presumably, adding more longitudinal predictors improves the predictive accuracy. However, the shared random effect model can be computationally difficult or prohibitive when a large number of longitudinal variables are used. In this paper, we study an alternative way of modeling the joint distribution of longitudinal and time-to-event data. Under this formulation, the log-likelihood involves no more than one-dimensional integration, regardless of the number of longitudinal variables in the model. Therefore, this model is particularly suitable in dynamic prediction problems with large number of longitudinal predictors. The model fitting can be implemented with tractable and stable computation by using a combination of pseudo maximum likelihood estimation, Expectation-Maximization algorithm, and convex optimization. We evaluate the proposed methodology and its predictive accuracy with varying number of longitudinal variables using simulations and data from a primary biliary cirrhosis study.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.