Incorporating temporal features of repeatedly measured covariates into tree-structured survival models.
Incorporating temporal features of repeatedly measured covariates into tree-structured survival models.
复制标题
将重复测量的协变量的时间特征纳入树结构生存模型。
DOI：
10.1002/bimj.201100013
复制
发表时间：
2012
期刊：
影响因子：
--
通讯作者：
Mulsant,BenoitH
中科院分区：
文献类型：
--
作者：
Wallace,MeredithL;Anderson,StewartJ;Mazumdar,Sati;Kong,Lan;Mulsant,BenoitH
树形结构生存方法经验性地识别了一系列基于协变量的二进制分离点，从而产生了一种算法，可以用于将新患者分类为危险组，并随后指导临床治疗决策。传统上，在树形结构的模型中只使用固定时间(例如基线)值。然而，本文考虑的情景中，重复测量多项式模型的时间特征，如斜率和/或曲率，对于区分风险组以预测未来结果是有用的。讨论了估计个体时间特征的固定效应和随机效应方法，并提出了将这些特征包括在树模型中和对新病例进行分类的方法。进行了模拟研究，以经验性地比较所提出的方法在各种模型设置下的预测精度。作为说明，树状结构的生存模型结合了老年抑郁症治疗的前四周内抑郁症状的线性变化率，用于识别可能从早期治疗策略改变中受益的老年人亚群。
Tree‐structured survival methods empirically identify a series of covariate‐based binary split points, resulting in an algorithm that can be used to classify new patients into risk groups and subsequently guide clinical treatment decisions. Traditionally, only fixed‐time (e.g. baseline) values are used in tree‐structured models. However, this manuscript considers the scenario where temporal features of a repeated measures polynomial model, such as the slope and/or curvature, are useful for distinguishing risk groups to predict future outcomes. Both fixed‐ and random‐effects methods for estimating individual temporal features are discussed, and methods for including these features in a tree model and classifying new cases are proposed. A simulation study is performed to empirically compare the predictive accuracies of the proposed methods in a wide variety of model settings. For illustration, a tree‐structured survival model incorporating the linear rate of change of depressive symptomatology during the first four weeks of treatment for late‐life depression is used to identify subgroups of older adults who may benefit from an early change in treatment strategy.
登录
查看更多内容
影响因子：
2
作者：
Dannegger, F
通讯作者：
Dannegger, F
影响因子：
2
作者：
Marubini, E;Morabito, A;Valsecchi, M G
通讯作者：
Valsecchi, M G
DOI：
--
发表时间：
1972
期刊：
--
影响因子：
--
作者：
D. Cox
通讯作者：
D. Cox
影响因子：
1.9
作者：
Huang, X;Chen, SD;Soong, SJ
通讯作者：
Soong, SJ
影响因子：
11.3
作者：
BUYSSE, DJ;REYNOLDS, CF;KUPFER, DJ
通讯作者：
KUPFER, DJ