| 期刊全稱 | Analysis of Multivariate Survival Data | | 影響因子2023 | Philip Hougaard | | 視頻video | http://file.papertrans.cn/157/156398/156398.mp4 | | 學(xué)科分類 | Statistics for Biology and Health | | 圖書封面 |  | | 影響因子 | Survival data or more general time-to-event data occur in many areas, including medicine, biology, engineering, economics, and demography, but previously standard methods have requested that all time variables are univariate and independent. This book extends the field by allowing for multivariate times. Applications where such data appear are survival of twins, survival of married couples and families, time to failure of right and left kidney for diabetic patients, life history data with time to outbreak of disease, complications and death, recurrent episodes of diseases and cross-over studies with time responses. As the field is rather new, the concepts and the possible types of data are described in detail and basic aspects of how dependence can appear in such data is discussed. Four different approaches to the analysis of such data are presented. The multi-state models where a life history is described as the subject moving from state to state is the most classical approach. The Markov models make up an important special case, but it is also described how easily more general models are set up and analyzed. Frailty models, which are random effects models for survival data, made | | Pindex | Book 2000 |
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