Dynamic prediction of the risk of a clinical event using longitudinally measured biomarkers or other prognostic information is important in clinical practice. We propose a new class of landmark survival models. The model takes the form of a linear transformation model, but allows all the model parameters to vary with the landmark time. This model includes many published landmark prediction models as special cases. We propose a unified local linear estimation framework to estimate time-varying model parameters. Simulation studies are conducted to evaluate the finite sample performance of the proposed method. We apply the methodology to a dataset from the African American Study of Kidney Disease and Hypertension and predict individual patient's risk of an adverse clinical event.

中文翻译：

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用于动态预测的里程碑式线性变换模型及其在慢性病纵向队列研究中的应用。


使用纵向测量的生物标志物或其他预后信息动态预测临床事件的风险在临床实践中非常重要。我们提出了一类新的具有里程碑意义的生存模型。该模型采用线性变换模型的形式，但允许所有模型参数随里程碑时间而变化。该模型包括许多已发布的里程碑预测模型作为特例。我们提出了一个统一的局部线性估计框架来估计时变模型参数。进行仿真研究以评估所提出方法的有限样本性能。我们将该方法应用于非裔美国人肾脏疾病和高血压研究的数据集，并预测个体患者发生不良临床事件的风险。