Deep learning for the dynamic prediction of multivariate longitudinal and survival data
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DOI: 10.1002/sim.9392
Abstract: The joint model for longitudinal and survival data improves time‐to‐event predictions by including longitudinal outcome variables in addition to baseline covariates. However, in practice, joint models may be limited by parametric assumptions in both the… read more here.
Keywords: survival data; time event; longitudinal survival; survival ... See more keywords
Joint modeling of longitudinal and survival data with a covariate subject to a limit of detection
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DOI: 10.1177/0962280217729573
Abstract: We develop and study an innovative method for jointly modeling longitudinal response and time-to-event data with a covariate subject to a limit of detection. The joint model assumes a latent process based on random effects… read more here.
Keywords: covariate; limit detection; longitudinal survival;
Assessing importance of biomarkers: A Bayesian joint modelling approach of longitudinal and survival data with semi-competing risks
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DOI: 10.1177/1471082x20933363
Abstract: Longitudinal biomarkers such as patient-reported outcomes (PROs) and quality of life (QOL) are routinely collected in cancer clinical trials or other studies. Joint modelling of PRO/QOL and survival data can provide a comparative assessment of… read more here.
Keywords: competing risks; longitudinal survival; semi competing; joint modelling ... See more keywords