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Partially linear mixed-effects joint models for skewed and missing longitudinal competing risks outcomes
文献类型:期刊
作者:Lu Tao[1]  Lu Minggen[2]  Wang Min[3]  Zhang Jun[4]  Dong Guang-Hui[5]  Xu Yong[6]  
机构:Department of Mathematics and Statistics, University of Nevada, Reno, NV, USA.;School of Community Health Sciences, University of Nevada, Reno, NV, USA.;Department of Mathematical Sciences, Michigan Technological University, Houghton, Michigan, USA.;Department of Preventive Medicine, Sun Yat-sen University, Guangzhou, China.;Department of Applied Mathematics, Northwestern Polytechnical University, Xi'an, China.
年:2019
期刊名称:Journal of biopharmaceutical statistics影响因子和分区
卷:29
期:6
页码范围:971-989
增刊:正刊
收录情况:(29252088)  
人气指数:33
浏览次数:33
关键词:Bayesian inference,competing risks,longitudinal survival data,partially linear mixed-effects models,proportional hazard models
摘要:
Longitudinal competing risks data frequently arise in clinical studies. Skewness and missingness are commonly observed for these data in practice. However, most joint models do not account for these data features. In this article, we propose partially linear mixed-effects joint models to analyze skew longitudinal competing risks data with missingness. In particular, to account for skewness, we replace the commonly assumed symmetric distributions by asymmetric distribution for model errors. To de ...More
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