A flexible B-spline model for multiple longitudinal biomarkers and survival.
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.
Brown
Elizabeth R
ER
Department of Biostatistics, University of Washington, Campus Mail Stop 357232, Seattle, Washington 98195-7232, USA. elizab@u.washington.edu
Ibrahim
Joseph G
JG
DeGruttola
Victor
V
D000163:Acquired Immunodeficiency Syndrome / Q000188:drug therapy / Q000401:mortality / Q000473:pathology; D000465:Algorithms; D019380:Anti-HIV Agents / Q000494:pharmacology; D015415:Biomarkers; D001699:Biometry; D018791:CD4 Lymphocyte Count / Q000706:statistics & numerical data; D018450:Disease Progression; D015658:HIV Infections / Q000188:drug therapy / Q000401:mortality* / Q000473:pathology*; D006801:Humans; D008137:Longitudinal Studies; D008390:Markov Chains; D009010:Monte Carlo Method; D016016:Proportional Hazards Models; D018709:Statistics, Nonparametric; D016019:Survival Analysis; D013997:Time Factors; D019562:Viral Load / Q000706:statistics & numerical data
D019380:Anti-HIV Agents; D015415:Biomarkers