Multivariate exponential survival trees and their application to tooth prognosis
Multivariate exponential survival trees and their application to tooth prognosis
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DOI：
10.1016/j.csda.2008.10.019
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发表时间：
2009-02-15
影响因子：
1.8
通讯作者：
Su, Xiaogang
中科院分区：
文献类型：
--
作者：
Fan, Juanjuan;Nunn, Martha E.;Su, Xiaogang
本文是关于发展规则的牙齿预后分配的基础上实际牙齿损失的VA牙科纵向研究。对各种临床因素对牙齿脱落的相对重要性进行排名也很有意义。提出了一种多变量生存树方法。该过程是建立在一个参数指数脆弱性模型，这导致更大的计算效率。我们采用了[LeBlanc，M.，克劳利，J.，1993.生存树的优良分裂。Journal of the American Statistical Association 88，457-467]以确定最佳树尺寸。此外，变量重要性方法被扩展到使用类似于[Breiman，L，2001.随机森林机器学习45，5-32]。模拟研究评估建议的树和变量的重要性方法。为了限制有意义的预后组的最终数量，采用合并算法来合并在牙齿存活中均质的终端节点。由此产生的预后规则和变量的重要性排名似乎提供了简单而清晰和有见地的解释。(C)2008 Elsevier B. V.保留所有权利。
This paper is concerned with developing rules for assignment of tooth prognosis based on actual tooth loss in the VA Dental Longitudinal Study. It is also of interest to rank the relative importance of various clinical factors for tooth loss. A multivariate survival tree procedure is proposed. The procedure is built on a parametric exponential frailty model, which leads to greater computational efficiency. We adopted the goodness-of-split pruning algorithm of [LeBlanc, M., Crowley, J., 1993. Survival trees by goodness of split. journal of the American Statistical Association 88, 457-467] to determine the best tree size. In addition, the variable importance method is extended to trees grown by goodness-of-fit using an algorithm similar to the random forest procedure in [Breiman, L, 2001. Random forests. Machine Learning 45, 5-32]. Simulation studies for assessing the proposed tree and variable importance methods are presented. To limit the final number of meaningful prognostic groups, an amalgamation algorithm is employed to merge terminal nodes that are homogeneous in tooth survival. The resulting prognosis rules and variable importance rankings seem to offer simple yet clear and insightful interpretations. (C) 2008 Elsevier B.V. All rights reserved.