审查(临床试验)
期望最大化算法
参数统计
数学
非线性系统
混合模型
事件(粒子物理)
参数化模型
最大化
算法
数学优化
计算机科学
统计
最大似然
物理
量子力学
作者
Cyprien Mbogning,Kevin Bleakley,Marc Lavielle
标识
DOI:10.1080/00949655.2013.878938
摘要
AbstractWe propose a nonlinear mixed-effects framework to jointly model longitudinal and repeated time-to-event data. A parametric nonlinear mixed-effects model is used for the longitudinal observations and a parametric mixed-effects hazard model for repeated event times. We show the importance for parameter estimation of properly calculating the conditional density of the observations (given the individual parameters) in the presence of interval and/or right censoring. Parameters are estimated by maximizing the exact joint likelihood with the stochastic approximation expectation–maximization algorithm. This workflow for joint models is now implemented in the Monolix software, and illustrated here on five simulated and two real datasets.Keywords: joint modelsmixed-effects modelsrepeated time-to-eventsmaximum likelihoodSAEM algorithm
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