Trees for correlated survival data by goodness of split, with applications to tooth prognosis
Trees for correlated survival data by goodness of split, with applications to tooth prognosis
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DOI：
10.1198/016214506000000438
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发表时间：
2006-09-01
影响因子：
3.7
通讯作者：
LeBlanc, Michael
中科院分区：
文献类型：
--
作者：
Fan, Juanjuan;Su, Xiao-Gang;LeBlanc, Michael
本文将回归树方法推广到相关生存数据，并应用于牙周病研究中客观预后分类规则的制定。稳健的对数秩统计量被用作分裂统计量，以测量节点间的生存差异，同时调整来自同一患者的故障时间之间的相关性。基于划分的生存函数估计收敛到真正的条件生存函数。使用所提出的方法分析了100名牙周病患者（2,509颗牙齿）的牙齿缺失数据。我们的目标是将每颗牙齿分配到五个预后类别之一（好，一般，差，可疑或无望）。在确定最佳大小的树后，使用合并程序来形成五个预后组。这里建立的预后规则可用于牙周病医生，一般牙医，和保险公司在制定适当的治疗计划，牙周病患者。
In this article the regression tree method is extended to correlated survival data and applied to the problem of developing objective prognostic classification rules in periodontal research. The robust logrank statistic is used as the splitting statistic to measure the between-node difference in survival, while adjusting for correlation among failure times from the same patient. The partition-based survival function estimator is shown to converge to the true conditional survival function. Tooth loss data from 100 periodontal patients (2,509 teeth) was analyzed using the proposed method. The goal is to assign each tooth to one of the five prognosis categories (good, fair, poor, questionable, or hopeless). After the best-sized tree was identified, an amalgamation procedure was used to form five prognostic groups. The prognostic rules established here may be used by periodontists, general dentists, and insurance companies in devising appropriate treatment plans for periodontal patients.