The versatility of multi-state models for the analysis of longitudinal data with unobservable features

被引：0
|
作者：
Vernon T. Farewell
Brian D. M. Tom
机构：
[1] Institute of Public Health,MRC Biostatistics Unit
来源：
Lifetime Data Analysis | 2014年 / 20卷
关键词：
Causal inference; Classification uncertainty; Informative missing data; Multi-state models; Time dependent explanatory variables;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Multi-state models provide a convenient statistical framework for a wide variety of medical applications characterized by multiple events and longitudinal data. We illustrate this through four examples. The potential value of the incorporation of unobserved or partially observed states is highlighted. In addition, joint modelling of multiple processes is illustrated with application to potentially informative loss to follow-up, mis-measured or missclassified data and causal inference.
引用
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页码：51 / 75
页数：24
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