BAYESIAN SEMIPARAMETRIC INFERENCE FOR MULTIVARIATE DOUBLY-INTERVAL-CENSORED DATA
BAYESIAN SEMIPARAMETRIC INFERENCE FOR MULTIVARIATE DOUBLY-INTERVAL-CENSORED DATA
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DOI：
10.1214/10-aoas368
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发表时间：
2010-12-01
影响因子：
1.8
通讯作者：
Quintana, Fernando
中科院分区：
文献类型：
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作者：
Jara, Alejandro;Lesaffre, Emmanuel;Quintana, Fernando
根据在比利时佛兰德斯进行的一项牙科纵向研究中获得的数据集，恒第一磨牙的联合龋齿时间分布被建模为协变量的函数。这涉及到对多变量连续双区间删失数据的分析，因为：(I)牙齿的出现时间和经历龋齿的时间是每年记录的，(Ii)同一儿童牙齿上的事件是相关的。为了对出现时间和龋齿发生次数的联合分布进行建模，我们提出了一个相依贝叶斯半参数模型。该方法的一个主要特点是，可以在不强加比例风险、附加风险、比例赔率或加速失效时间等假设的情况下估计生存曲线。
Based on a data set obtained in a dental longitudinal study, conducted in Flanders (Belgium), the joint time to caries distribution of permanent first molars was modeled as a function of covariates. This involves an analysis of multivariate continuous doubly-interval-censored data since: (i) the emergence time of a tooth and the time it experiences caries were recorded yearly, and (ii) events on teeth of the same child are dependent. To model the joint distribution of the emergence times and the times to caries, we propose a dependent Bayesian semiparametric model. A major feature of the proposed approach is that survival curves can be estimated without imposing assumptions such as proportional hazards, additive hazards, proportional odds or accelerated failure time.