ArticleStatistical methods in medical research2016
Joint modeling of longitudinal data and discrete-time survival outcome.
Article in Statistical methods in medical research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Joint Modeling of Geometric Features of Longitudinal Process and Discrete Survival Time Measured on Nested Timescales: An Application to Fecundity Studies.Statistics in biosciences · 2024Article
- Effect of suicidality on clinical and behavioural outcomes in HIV positive adults in Uganda.PloS one · 2021Article
- Harnessing repeated measurements of predictor variables for clinical risk prediction: a review of existing methods.Diagnostic and prognostic research · 2020Review
- The Role of Adolescence in Development Paths Toward Suicide: Specificities and Shaping of Adversity Trajectories.Frontiers in psychiatry · 2020Article
- Joint Models for Multiple Longitudinal Processes and Time-to-event Outcome.Journal of statistical computation and simulation · 2016Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
A predictive joint shared parameter model is proposed for discrete time-to-event and longitudinal data. A discrete survival model with frailty and a generalized linear mixed model for the longitudinal data are joined to predict the probability of events. This joint model focuses on predicting discrete time-to-event outcome, taking advantage of repeated measurements. We show that the probability of an event in a time window can be more precisely predicted by incorporating the longitudinal measurements. The model was investigated by comparison with a two-step model and a discrete-time survival model. Results from both a study on the occurrence of tuberculosis and simulated data show that the joint model is superior to the other models in discrimination ability, especially as the latent variables related to both survival times and the longitudinal measurements depart from 0.
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Registered trials
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