A flexible link for joint modelling longitudinal and survival data accounting for individual longitudinal heterogeneity
暂无分享,去创建一个
[1] Rui Zhuang,et al. Measuring Surrogacy in Clinical Research , 2019, Statistics in Biosciences.
[2] Yangxin Huang,et al. Quantile regression-based Bayesian joint modeling analysis of longitudinal–survival data, with application to an AIDS cohort study , 2019, Lifetime Data Analysis.
[3] Stacia DeSantis,et al. Bayesian quantile regression joint models: Inference and dynamic predictions , 2018, Statistical methods in medical research.
[4] Rui Martins,et al. Joint analysis of longitudinal and survival AIDS data with a spatial fraction of long‐term survivors: A Bayesian approach , 2017, Biometrical journal. Biometrische Zeitschrift.
[5] Rui Martins,et al. Bayesian joint modeling of longitudinal and spatial survival AIDS data , 2016, Statistics in medicine.
[6] Yangxin Huang,et al. A Bayesian mixture of semiparametric mixed‐effects joint models for skewed‐longitudinal and time‐to‐event data , 2015, Statistics in medicine.
[7] Naisyin Wang,et al. Joint modeling of cross‐sectional health outcomes and longitudinal predictors via mixtures of means and variances , 2015, Biometrics.
[8] Alessio Farcomeni,et al. Longitudinal quantile regression in the presence of informative dropout through longitudinal–survival joint modeling , 2014, Statistics in medicine.
[9] Nian-Sheng Tang,et al. Semiparametric Bayesian joint models of multivariate longitudinal and survival data , 2014, Comput. Stat. Data Anal..
[10] Dimitris Rizopoulos,et al. The R Package JMbayes for Fitting Joint Models for Longitudinal and Time-to-Event Data using MCMC , 2014, 1404.7625.
[11] Kholoud Porter,et al. Impact on life expectancy of HIV-1 positive individuals of CD4+ cell count and viral load response to antiretroviral therapy , 2014, AIDS.
[12] Lei Liu,et al. Joint model of recurrent events and a terminal event with time‐varying coefficients , 2014, Biometrical journal. Biometrische Zeitschrift.
[13] Yangxin Huang,et al. Jointly modeling time-to-event and longitudinal data: a Bayesian approach , 2014, Stat. Methods Appl..
[14] Aki Vehtari,et al. Understanding predictive information criteria for Bayesian models , 2013, Statistics and Computing.
[15] David J. Lunn,et al. The BUGS Book: A Practical Introduction to Bayesian Analysis , 2013 .
[16] Hongtu Zhu,et al. Bayesian Influence Measures for Joint Models for Longitudinal and Survival Data , 2012, Biometrics.
[17] D. Rizopoulos. Event Time Event Time , 2012 .
[18] Rajeshwari Sundaram,et al. A Joint Mixed Effects Dispersion Model for Menstrual Cycle Length and Time‐to‐Pregnancy , 2012, Biometrics.
[19] Dimitris Rizopoulos,et al. A Bayesian semiparametric multivariate joint model for multiple longitudinal outcomes and a time‐to‐event , 2011, Statistics in medicine.
[20] Feng Gao,et al. A joint-modeling approach to assess the impact of biomarker variability on the risk of developing clinical outcome , 2011, Stat. Methods Appl..
[21] Wesley O Johnson,et al. Predictive comparison of joint longitudinal-survival modeling: a case study illustrating competing approaches , 2011, Lifetime data analysis.
[22] Paul H. C. Eilers,et al. Splines, knots, and penalties , 2010 .
[23] Ronald Christensen,et al. Bayesian Ideas and Data Analysis: An Introduction for Scientists and Statisticians , 2010 .
[24] Sumio Watanabe,et al. Asymptotic Equivalence of Bayes Cross Validation and Widely Applicable Information Criterion in Singular Learning Theory , 2010, J. Mach. Learn. Res..
[25] Geert Verbeke,et al. Fully exponential Laplace approximations for the joint modelling of survival and longitudinal data , 2009 .
[26] M. J. Costa,et al. Parametrization and penalties in spline models with an application to survival analysis , 2009, Comput. Stat. Data Anal..
[27] Benjamin Hofner,et al. Building Cox-type structured hazard regression models with time-varying effects , 2011 .
[28] Xiao Song,et al. Semiparametric Approaches for Joint Modeling of Longitudinal and Survival Data with Time‐Varying Coefficients , 2008, Biometrics.
[29] L. Fahrmeir,et al. A Mixed Model Approach for Geoadditive Hazard Regression , 2007 .
[30] F. Hsieh,et al. Joint Modeling of Survival and Longitudinal Data: Likelihood Approach Revisited , 2006, Biometrics.
[31] L. Fahrmeir,et al. Geoadditive Survival Models , 2006 .
[32] Andreas Brezger,et al. Generalized structured additive regression based on Bayesian P-splines , 2006, Comput. Stat. Data Anal..
[33] F. Hsieh,et al. Joint modelling of accelerated failure time and longitudinal data , 2005 .
[34] Joseph G. Ibrahim,et al. BAYESIAN METHODS FOR JOINT MODELING OF LONGITUDINAL AND SURVIVAL DATA WITH APPLICATIONS TO CANCER VACCINE TRIALS , 2004 .
[35] A. Gelman. Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper) , 2004 .
[36] S. Lang,et al. Bayesian P-Splines , 2004 .
[37] Bradley P Carlin,et al. Separate and Joint Modeling of Longitudinal and Event Time Data Using Standard Computer Packages , 2004 .
[38] Bradley P. Carlin,et al. Bayesian measures of model complexity and fit , 2002 .
[39] J. Kalbfleisch,et al. The Statistical Analysis of Failure Time Data: Kalbfleisch/The Statistical , 2002 .
[40] R Henderson,et al. Joint modelling of longitudinal measurements and event time data. , 2000, Biostatistics.
[41] Andrew Thomas,et al. WinBUGS - A Bayesian modelling framework: Concepts, structure, and extensibility , 2000, Stat. Comput..
[42] R H Lyles,et al. Adjusting for measurement error to assess health effects of variability in biomarkers. Multicenter AIDS Cohort Study. , 1999, Statistics in medicine.
[43] J. Raz,et al. Linear mixed models with heterogeneous within-cluster variances. , 1997, Biometrics.
[44] M. Wulfsohn,et al. A joint model for survival and longitudinal data measured with error. , 1997, Biometrics.
[45] Bradley P. Carlin,et al. BAYES AND EMPIRICAL BAYES METHODS FOR DATA ANALYSIS , 1996, Stat. Comput..
[46] D. Thomas,et al. Simultaneously modelling censored survival data and repeatedly measured covariates: a Gibbs sampling approach. , 1996, Statistics in medicine.
[47] D. Lin,et al. Evaluating the role of CD4-lymphocyte counts as surrogate endpoints in human immunodeficiency virus clinical trials. , 1993, Statistics in medicine.
[48] A. Gelfand,et al. Hierarchical Bayes Models for the Progression of HIV Infection Using Longitudinal CD4 T-Cell Numbers , 1992 .
[49] J. Kalbfleisch,et al. The Statistical Analysis of Failure Time Data , 1980 .
[50] D. Rubin. INFERENCE AND MISSING DATA , 1975 .
[51] D. Cox. Regression Models and Life-Tables , 1972 .