基于纵向生存数据联合模型的肝硬化数据应用研究
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摘要
纵向生存数据基本的联合模型都包含一个纵向结果和一个生存结果,该文基于混合效应模型和Cox比例风险模型构建纵向生存数据的标准联合模型和广义联合模型,并利用原发性胆汁性肝硬化(PBC)数据进行建模对比分析,结果表明联合建模结果优于单独生存建模或纵向建模结果,广义联合模型优于标准联合模型.
The basic joint model of longitudinal data and survival data contains a longitudinal result and a survival result.This paper constructs the standard joint model and the generalized joint model of longitudinal data and survival data based on the mixed effect model and the Cox proportional hazard model.A comparative analysis of the primary biliary cirrhosis(PBC)data show that joint analysis is better than single analysis,and the generalized joint model is better than the standard joint model.
The basic joint model of longitudinal data and survival data contains a longitudinal result and a survival result.This paper constructs the standard joint model and the generalized joint model of longitudinal data and survival data based on the mixed effect model and the Cox proportional hazard model.A comparative analysis of the primary biliary cirrhosis(PBC)data show that joint analysis is better than single analysis,and the generalized joint model is better than the standard joint model.
引文
[1]李高荣,杨宜平.纵向数据半参数模型[M].北京:科学出版社,2015.
[2] XU G,CARLIN B P.Separate and joint modeling of longitudinal and event time data using standard computer packages[J].American Statistician,2004,58(1):16-24.
[3] FAUCETT C L,THOMAS D C.Simultaneously modelling censored survival data and repeatedly measured covari-ates:a Gibbs sampling approach[J].Statistics in Medicine,1996,15(15):1663.
[4] WULFSOHN M S,TSIATIS A A.A joint model for survival and longitudinal data measured with error[J].Biometrics,1997,53(1):330.
[5] BROWN E R,IBRAHIM J G,DEGRUTTOLA V.A flexible B-spline model for multiple longitudinal biomarkers and survival[J].Biometrics,2015,61(1):64-73.
[6] BROWN E R.Assessing the association between trends in a biomarker and risk of event with an application in pediatric HIV/AIDS[J].Annals of Applied Statistics,2009,3(3):1163-1182.
[7] RIZOPOULOS D,GHOSH P.A Bayesian semiparametric multivariate joint model for multiple longitudinal outcomes and a time-to-event[J].Statistics in Medicine,2011,30(12):1366-1380.
[8] RIZOPOULOS D.Joint models for longitudinal and time-to-event data:with applications in R[J].Australian&New Zealand Journal of Statistics,2012,55(2):198-199.
[9] GERSHWIN M E,MACKAY I R.The causes of primary biliary cirrhosis:Convenient and inconvenient truths[J].Hepatology,2010,47(2):737-745.
[10]彭非,王伟.生存分析[M].北京:中国人民大学出版社,2004.
[11]吴喜之.复杂数据统计方法—基于R的应用:高等院校研究生用书[M].2版.北京:中国人民大学出版社,2013.
[2] XU G,CARLIN B P.Separate and joint modeling of longitudinal and event time data using standard computer packages[J].American Statistician,2004,58(1):16-24.
[3] FAUCETT C L,THOMAS D C.Simultaneously modelling censored survival data and repeatedly measured covari-ates:a Gibbs sampling approach[J].Statistics in Medicine,1996,15(15):1663.
[4] WULFSOHN M S,TSIATIS A A.A joint model for survival and longitudinal data measured with error[J].Biometrics,1997,53(1):330.
[5] BROWN E R,IBRAHIM J G,DEGRUTTOLA V.A flexible B-spline model for multiple longitudinal biomarkers and survival[J].Biometrics,2015,61(1):64-73.
[6] BROWN E R.Assessing the association between trends in a biomarker and risk of event with an application in pediatric HIV/AIDS[J].Annals of Applied Statistics,2009,3(3):1163-1182.
[7] RIZOPOULOS D,GHOSH P.A Bayesian semiparametric multivariate joint model for multiple longitudinal outcomes and a time-to-event[J].Statistics in Medicine,2011,30(12):1366-1380.
[8] RIZOPOULOS D.Joint models for longitudinal and time-to-event data:with applications in R[J].Australian&New Zealand Journal of Statistics,2012,55(2):198-199.
[9] GERSHWIN M E,MACKAY I R.The causes of primary biliary cirrhosis:Convenient and inconvenient truths[J].Hepatology,2010,47(2):737-745.
[10]彭非,王伟.生存分析[M].北京:中国人民大学出版社,2004.
[11]吴喜之.复杂数据统计方法—基于R的应用:高等院校研究生用书[M].2版.北京:中国人民大学出版社,2013.