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• W2107408488 abstract "Often when jointly modeling longitudinal and survival data, we are interested in a multivariate longitudinal measure that may not fit well by linear models. To overcome this problem, we propose a joint longitudinal and survival model that has a nonparametric model for the longitudinal markers. We use cubic B-splines to specify the longitudinal model and a proportional hazards model to link the longitudinal measures to the hazard. To fit the model, we use a Markov chain Monte Carlo algorithm. We select the number of knots for the cubic B-spline model using the Conditional Predictive Ordinate (CPO) and the Deviance Information Criterion (DIC). The method and model selection approach are validated in a simulation. We apply this method to examine the link between viral load, CD4 count, and time to event in data from an AIDS clinical trial. The cubic B-spline model provides a good fit to the longitudinal data that could not be obtained with simple parametric models." @default.
• W2107408488 created "2016-06-24" @default.
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• W2107408488 date "2005-03-01" @default.
• W2107408488 modified "2023-09-24" @default.
• W2107408488 title "A Flexible B-Spline Model for Multiple Longitudinal Biomarkers and Survival" @default.
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• W2107408488 doi "https://doi.org/10.1111/j.0006-341x.2005.030929.x" @default.
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