Joint latent class models for longitudinal and time-to-event data: a review.
Joint latent class models for longitudinal and time-to-event data: a review.
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DOI：
10.1177/0962280212445839
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发表时间：
2014-03
影响因子：
2.3
通讯作者：
Jacqmin-Gadda H
中科院分区：
文献类型：
--
作者：
Proust-Lima C;Séne M;Taylor JM;Jacqmin-Gadda H
联合建模领域的大多数统计发展都集中在共享的随机效应模型上，其中包括纵向标记的特征作为事件时间模型的预测因子。一种不太为人所知的方法是联合潜在类别模型，该模型假设潜在类别结构完全捕获了纵向标记轨迹与事件风险之间的相关性。由于它在模拟纵向标记和事件时间之间的依赖关系方面的灵活性，以及它包含协变量的能力，联合潜在类模型可能特别适合于预测问题。本文旨在概述联合潜在类建模，特别是在预测背景下。作者介绍了该模型，讨论了估计和拟合优度，并将其与共享随机效应模型进行了比较。然后，给出了基于联合潜类模型的动态预测工具，以及评估其动态预测精度的措施。详细说明的方法是在预测前列腺癌放疗后复发的背景下给出的基于前列腺特异性抗原的重复测量。
Most statistical developments in the joint modelling area have focused on the shared random-effect models that include characteristics of the longitudinal marker as predictors in the model for the time-to-event. A less well-known approach is the joint latent class model which consists in assuming that a latent class structure entirely captures the correlation between the longitudinal marker trajectory and the risk of the event. Owing to its flexibility in modelling the dependency between the longitudinal marker and the event time, as well as its ability to include covariates, the joint latent class model may be particularly suited for prediction problems. This article aims at giving an overview of joint latent class modelling, especially in the prediction context. The authors introduce the model, discuss estimation and goodness-of-fit, and compare it with the shared random-effect model. Then, dynamic predictive tools derived from joint latent class models, as well as measures to evaluate their dynamic predictive accuracy, are presented. A detailed illustration of the methods is given in the context of the prediction of prostate cancer recurrence after radiation therapy based on repeated measures of Prostate Specific Antigen.
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影响因子：
2.1
作者：
Henderson, R;Diggle, P;Dobson, A
通讯作者：
Dobson, A
DOI：
10.1093/biostatistics/1.4.465
发表时间：
2000-12-01
期刊：
Biostatistics (Oxford, England)
影响因子：
--
作者：
Henderson, R;Diggle, P;Dobson, A
通讯作者：
Dobson, A
影响因子：
2
作者：
Diggle, Peter J.;Sousa, Ines;Chetwynd, Amanda G.
通讯作者：
Chetwynd, Amanda G.
影响因子：
1.7
作者：
Gerds, Thomas A.;Schumacher, Martin
通讯作者：
Schumacher, Martin
影响因子：
2.1
作者：
Muthén, B;Brown, CH;Liao, J
通讯作者：
Liao, J