A flexible link for joint modelling longitudinal and survival data accounting for individual longitudinal heterogeneity

[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 .