Dynamic predictions using flexible joint models of longitudinal and time-to-event data.
Dynamic predictions using flexible joint models of longitudinal and time-to-event data.
复制标题
使用纵向和事件时间数据的灵活联合模型进行动态预测。
DOI：
10.1002/sim.7209
复制
发表时间：
2017-04-30
影响因子：
2
通讯作者：
Su L
中科院分区：
文献类型：
--
作者：
Barrett J;Su L
纵向和事件间隔时间数据的联合模型与许多临床研究特别相关，在这些临床研究中，纵向生物标记物可能与事件间隔时间结果高度相关。这一领域的一个前沿研究方向是根据所有可用的生物标志物信息动态预测患者预后(例如，生存概率)，这是最近在分层/个性化医学倡议的推动下进行的。由于这些动态预测是个性化的，因此需要灵活的模型来适当地描述每个个体的纵向轨迹。在这篇文章中，我们提出了一个新的联合模型，它使用个体水平的惩罚样条线(P-Splines)来灵活地刻画纵向过程和事件间隔时间过程的协同进化。我们方法的一个重要特点是，由于给定观测数据的随机P-样条系数的后验分布是多变量的斜正态分布，所以对生存概率的动态预测是直接的。所提出的方法用HIV流行病学研究的数据进行了说明。仿真结果表明，与已有方法相比，该模型具有更好的动态预测性能。©2017作者。约翰·威利父子有限公司出版的医学统计数据。
Joint models for longitudinal and time‐to‐event data are particularly relevant to many clinical studies where longitudinal biomarkers could be highly associated with a time‐to‐event outcome. A cutting‐edge research direction in this area is dynamic predictions of patient prognosis (e.g., survival probabilities) given all available biomarker information, recently boosted by the stratified/personalized medicine initiative. As these dynamic predictions are individualized, flexible models are desirable in order to appropriately characterize each individual longitudinal trajectory. In this paper, we propose a new joint model using individual‐level penalized splines (P‐splines) to flexibly characterize the coevolution of the longitudinal and time‐to‐event processes. An important feature of our approach is that dynamic predictions of the survival probabilities are straightforward as the posterior distribution of the random P‐spline coefficients given the observed data is a multivariate skew‐normal distribution. The proposed methods are illustrated with data from the HIV Epidemiology Research Study. Our simulation results demonstrate that our model has better dynamic prediction performance than other existing approaches. © 2017 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.