区间删失数据下多状态模型的惩罚极大似然估计
Penalised maximum likelihood estimation in multistate models for interval-censored data
摘要：
多状态模型可以用来描述随着时间的推移跨状态的转换。在过渡的区间删失时间的存在下，使用转移概率构造似然。使用转换的比例风险模型指定模型。时间依赖性通常由参数模型定义，这可能限制太多。样条非参数危险规范允许灵活的时间依赖性建模，而无需进行强有力的模型假设。惩罚极大似然被用来估计模型。选择最佳的平滑量是具有挑战性的，因为该问题涉及多个惩罚。本文提出了一种自动有效的方法，在区间删失的情况下，用样条函数估计多状态模型。通过数据分析和仿真研究说明了该方法。
英文摘要：
Multistate models can be used to describe transitions over time across states. In the presence of interval-censored times for transitions, the likelihood is constructed using transition probabilities. Models are specified using proportional hazards model for the transitions. Time-dependency is usually defined by parametric models, which can be too restrictive. Nonparametric hazards specification with splines allow for flexible modelling of time-dependency without making strong model assumptions. Penalised maximum likelihood is used to estimate the models. Selecting the optimal amount of smoothing is challenging as the problem involves multiple penalties. We propose an automatic and efficient method to estimate multistate models with splines in the presence of interval-censoring. The method is illustrated with a data analysis and a simulation study.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
