Brown Elizabeth R, Ibrahim Joseph G, DeGruttola Victor
Department of Biostatistics, University of Washington, Campus Mail Stop 357232, Seattle, Washington 98195-7232, USA.
Biometrics. 2005 Mar;61(1):64-73. doi: 10.1111/j.0006-341X.2005.030929.x.
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.
在联合建模纵向数据和生存数据时，我们常常会关注一种多元纵向测量指标，而线性模型可能无法很好地拟合该指标。为克服这一问题，我们提出了一种联合纵向和生存模型，该模型对纵向标记采用非参数模型。我们使用三次B样条来指定纵向模型，并使用比例风险模型将纵向测量指标与风险联系起来。为拟合该模型，我们使用马尔可夫链蒙特卡罗算法。我们使用条件预测纵坐标（CPO）和偏差信息准则（DIC）来选择三次B样条模型的节点数量。该方法和模型选择方法在模拟中得到了验证。我们将此方法应用于一项艾滋病临床试验数据，以检验病毒载量、CD4细胞计数与事件发生时间之间的联系。三次B样条模型能很好地拟合纵向数据，而简单的参数模型无法做到这一点。
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