Bayesian joint modeling of ordinal longitudinal measurements and competing risks survival data for analysing Tehran Lipid and Glucose Study

ABSTRACT In this paper, joint modeling of longitudinal ordinal measurements and time to some events of interest as competing risks is discussed. For this purpose, a latent variable sub-model under linear mixed-effects assumption is considered for modeling ordinal longitudinal measurements. Also, a Weibull cause-specific sub-model is used to model competing risks data. These two sub-models are simultaneously considered in a unique model by a shared parameter model framework. Some simulation studies are performed for illustration of the proposed approaches; also, the proposed approaches are used for analyzing 15 years of lipid and glucose follow-up study in Tehran.

[1]  Gang Li,et al.  A Bayesian approach to joint analysis of longitudinal measurements and competing risks failure time data , 2007, Statistics in medicine.

[2]  Joseph D. Conklin.  Classical Competing Risks , 2002, Technometrics.

[3]  T. Baghfalaki,et al.  Joint modeling of mixed skewed continuous and ordinal longitudinal responses: a Bayesian approach , 2015 .

[4]  Taban Baghfalaki,et al.  A BAYESIAN APPROACH FOR JOINT MODELING OF SKEW-NORMAL LONGITUDINAL MEASUREMENTS AND TIME TO EVENT DATA , 2015 .

[5]  F. Hadaegh,et al.  C-reactive protein in risk prediction of cardiovascular outcomes: Tehran Lipid and Glucose Study. , 2007, International journal of cardiology.

[6]  D. Cox,et al.  A General Definition of Residuals , 1968 .

[7]  M. Crowder.  Multivariate Survival Analysis and Competing Risks , 2012 .

[8]  D. Thomas,et al.  Simultaneously modelling censored survival data and repeatedly measured covariates: a Gibbs sampling approach. , 1996, Statistics in medicine.

[9]  Marie Davidian,et al.  A Semiparametric Likelihood Approach to Joint Modeling of Longitudinal and Time‐to‐Event Data , 2002, Biometrics.

[10]  Melania Pintilie,et al.  Competing Risks: A Practical Perspective , 2006 .

[11]  R. L. Prentice,et al.  Retrospective studies and failure time models , 1978 .

[12]  S. Zeger,et al.  Joint analysis of longitudinal data comprising repeated measures and times to events , 2001 .

[13]  D. Rubin,et al.  Inference from Iterative Simulation Using Multiple Sequences , 1992 .

[14]  John O'Quigley,et al.  Joint analysis of multi‐level repeated measures data and survival: an application to the end stage renal disease (ESRD) data , 2008, Statistics in medicine.

[15]  Donglin zeng,et al.  Simultaneous Modelling of Survival and Longitudinal Data with an Application to Repeated Quality of Life Measures , 2005, Lifetime data analysis.

[16]  Bradley P. Carlin,et al.  Bayesian measures of model complexity and fit , 2002 .

[17]  R. Elashoff,et al.  An approach to joint analysis of longitudinal measurements and competing risks failure time data , 2007, Statistics in medicine.

[18]  Cécile Proust-Lima,et al.  Joint modeling of repeated multivariate cognitive measures and competing risks of dementia and death: a latent process and latent class approach , 2014, Statistics in medicine.

[19]  J. Ibrahim,et al.  A Bayesian semiparametric joint hierarchical model for longitudinal and survival data. , 2003, Biometrics.

[20]  Gang Li,et al.  Joint modeling of longitudinal ordinal data and competing risks survival times and analysis of the NINDS rt‐PA stroke trial , 2010, Statistics in medicine.

[21]  Aki Vehtari,et al.  Discussion on the paper by Spiegelhalter, Best, Carlin and van der Linde , 2002 .

[22]  T. Baghfalaki,et al.  A Bayesian conditional model for bivariate mixed ordinal and skew continuous longitudinal responses using quantile regression , 2018 .

[23]  Xihong Lin,et al.  Semiparametric Modeling of Longitudinal Measurements and Time‐to‐Event Data–A Two‐Stage Regression Calibration Approach , 2008, Biometrics.

[24]  Eleni-Rosalina Andrinopoulou,et al.  Joint modeling of two longitudinal outcomes and competing risk data , 2014, Statistics in medicine.

[25]  Damon Berridge,et al.  Joint modeling of multivariate longitudinal mixed measurements and time to event data using a Bayesian approach , 2014 .

[26]  R Henderson,et al.  Joint modelling of longitudinal measurements and event time data. , 2000, Biostatistics.

[27]  M Ganjali,et al.  Bayesian Joint Modeling of Longitudinal Measurements and Time-to-Event Data Using Robust Distributions , 2014, Journal of biopharmaceutical statistics.

[28]  Chi-Hong Tseng,et al.  Joint analysis of bivariate longitudinal ordinal outcomes and competing risks survival times with nonparametric distributions for random effects , 2012, Statistics in medicine.

[29]  M. Wulfsohn,et al.  A joint model for survival and longitudinal data measured with error. , 1997, Biometrics.

[30]  Dimitris Rizopoulos,et al.  Joint Models for Longitudinal and Time-to-Event Data: With Applications in R , 2012 .

[31]  V. De Gruttola,et al.  Modelling progression of CD4-lymphocyte count and its relationship to survival time. , 1994, Biometrics.

[32]  Damon Berridge,et al.  Robust joint modeling of longitudinal measurements and time to event data using normal/independent distributions: A Bayesian approach , 2013, Biometrical journal. Biometrische Zeitschrift.

[33]  T. Baghfalaki,et al.  Bayesian quantile regression for analyzing ordinal longitudinal responses in the presence of non-ignorable missingness , 2018 .

[34]  G. Verbeke,et al.  A shared parameter model of longitudinal measurements and survival time with heterogeneous random-effects distribution , 2017 .

[35]  Bradley P. Carlin,et al.  BAYES AND EMPIRICAL BAYES METHODS FOR DATA ANALYSIS , 1996, Stat. Comput..

[36]  F. Hsieh,et al.  Joint modelling of accelerated failure time and longitudinal data , 2005 .