Joint modeling of longitudinal data and discrete-time survival outcome.
Joint modeling of longitudinal data and discrete-time survival outcome.
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DOI：
10.1177/0962280213490342
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发表时间：
2016-08
影响因子：
2.3
通讯作者：
Tuberculosis Research Unit (TBRU)
中科院分区：
文献类型：
--
作者：
Qiu F;Stein CM;Elston RC;Tuberculosis Research Unit (TBRU)
针对离散事件时间和纵向数据提出了预测联合共享参数模型。将具有脆弱性的离散生存模型和纵向数据的广义线性混合模型相结合来预测事件的概率。该联合模型侧重于利用重复测量来预测离散的事件时间结果。我们表明，通过结合纵向测量可以更精确地预测时间窗口内事件的概率。通过与两步模型和离散时间生存模型进行比较来研究该模型。结核病发生研究和模拟数据的结果表明，联合模型在区分能力上优于其他模型，特别是当与生存时间和纵向测量相关的潜在变量都偏离0时。
A predictive joint shared parameter model is proposed for discrete time-to-event and longitudinal data. A discrete survival model with frailty and a generalized linear mixed model for the longitudinal data are joined to predict the probability of events. This joint model focuses on predicting discrete time-to-event outcome, taking advantage of repeated measurements. We show that the probability of an event in a time window can be more precisely predicted by incorporating the longitudinal measurements. The model was investigated by comparison with a two-step model and a discrete time survival model. Results from both a study on the occurrence of tuberculosis and simulated data show that the joint model is superior to the other models in discrimination ability, especially as the latent variables related to both survival times and the longitudinal measurements depart from 0.
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影响因子：
37.8
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影响因子：
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