Bayesian joint modeling for assessing the progression of chronic kidney disease in children

Joint models are rich and flexible models for analyzing longitudinal data with nonignorable missing data mechanisms. This article proposes a Bayesian random-effects joint model to assess the evolution of a longitudinal process in terms of a linear mixed-effects model that accounts for heterogeneity between the subjects, serial correlation, and measurement error. Dropout is modeled in terms of a survival model with competing risks and left truncation. The model is applied to data coming from ReVaPIR, a project involving children with chronic kidney disease whose evolution is mainly assessed through longitudinal measurements of glomerular filtration rate.

[1]  Noel A Cressie,et al.  Statistics for Spatial Data. , 1992 .

[2]  D. J. Spiegelhalter,et al.  Identifying outliers in Bayesian hierarchical models: a simulation-based approach , 2007 .

[3]  Susan L Furth,et al.  New equations to estimate GFR in children with CKD. , 2009, Journal of the American Society of Nephrology : JASN.

[4]  Martin Schumacher,et al.  Empirical Transition Matrix of Multi-State Models: The etm Package , 2011 .

[5]  H Putter,et al.  Tutorial in biostatistics: competing risks and multi‐state models , 2007, Statistics in medicine.

[6]  Tao Lu.  Bayesian nonparametric mixed-effects joint model for longitudinal-competing risks data analysis in presence of multiple data features , 2017, Statistical methods in medical research.

[7]  Jun Yan.  Survival Analysis: Techniques for Censored and Truncated Data , 2004 .

[8]  J. Kalbfleisch,et al.  The Statistical Analysis of Failure Time Data: Kalbfleisch/The Statistical , 2002 .

[9]  A. Gelfand,et al.  Hierarchical Bayes Models for the Progression of HIV Infection Using Longitudinal CD4 T-Cell Numbers , 1992 .

[10]  C. Robert,et al.  Bayesian Modeling Using WinBUGS , 2009 .

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

[12]  Yongseok Park,et al.  Real‐Time Individual Predictions of Prostate Cancer Recurrence Using Joint Models , 2013, Biometrics.

[13]  Raymond J. Carroll,et al.  Estimation and comparison of changes in the presence of informative right censoring by modeling the censoring process , 1988 .

[14]  P. Diggle,et al.  Analysis of Longitudinal Data. , 1997 .

[15]  C. Kendziorski,et al.  The efficiency of pooling mRNA in microarray experiments. , 2003, Biostatistics.

[16]  Bradley P Carlin,et al.  Separate and Joint Modeling of Longitudinal and Event Time Data Using Standard Computer Packages , 2004 .

[17]  George E. P. Box,et al.  Sampling and Bayes' inference in scientific modelling and robustness , 1980 .

[18]  Geert Molenberghs,et al.  Joint models for longitudinal data: Introduction and overview , 2008 .

[19]  L. Rabiner,et al.  An introduction to hidden Markov models , 1986, IEEE ASSP Magazine.

[20]  A. Doucet,et al.  Particle Markov chain Monte Carlo methods , 2010 .

[21]  A. Doucet,et al.  A Tutorial on Particle Filtering and Smoothing: Fifteen years later , 2008 .

[22]  Jean-François Dartigues,et al.  Random Changepoint Model for Joint Modeling of Cognitive Decline and Dementia , 2006, Biometrics.

[23]  Rizopoulos Dimitris,et al.  Joint Modeling of Longitudinal and Time-to-Event Data , 2014 .

[24]  V. Chadha,et al.  Chronic kidney disease in children: the global perspective , 2007, Pediatric Nephrology.

[25]  Ning Li,et al.  A Joint Model for Longitudinal Measurements and Survival Data in the Presence of Multiple Failure Types , 2008, Biometrics.

[26]  J. Gentle,et al.  Special Section: Teaching Computational Statistics , 2004 .

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

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

[29]  Charles E. McCulloch,et al.  Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization. , 2004, The New England journal of medicine.

[30]  M. J. Bayarri,et al.  P Values for Composite Null Models , 2000 .

[31]  Dimitris Rizopoulos,et al.  Dynamic Predictions and Prospective Accuracy in Joint Models for Longitudinal and Time‐to‐Event Data , 2011, Biometrics.

[32]  F. B. Hildebrand,et al.  Introduction To Numerical Analysis , 1957 .

[33]  P. Moral,et al.  Sequential Monte Carlo samplers , 2002, cond-mat/0212648.

[34]  F. Assadi,et al.  The epidemic of pediatric chronic kidney disease: the danger of skepticism. , 2012, Journal of nephropathology.

[35]  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.

[36]  Martyn Plummer,et al.  JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling , 2003 .

[37]  Odd O. Aalen,et al.  An introduction to survival and event history analysis , 2008 .

[38]  M S Pepe,et al.  Kaplan-Meier, marginal or conditional probability curves in summarizing competing risks failure time data? , 1993, Statistics in medicine.

[39]  Yan Wang,et al.  Jointly Modeling Longitudinal and Event Time Data With Application to Acquired Immunodeficiency Syndrome , 2001 .

[40]  Geert Verbeke,et al.  Conditional Linear Mixed Models , 2001 .

[41]  R. Kolamunnage-Dona,et al.  Joint modelling of longitudinal and competing risks data , 2008, Statistics in medicine.

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

[43]  Sylvia Frühwirth-Schnatter,et al.  Finite Mixture and Markov Switching Models , 2006 .

[44]  Jane-Ling Wang,et al.  A comparison of hazard rate estimators for left truncated and right censored data , 1992 .

[45]  Laurence L. George,et al.  The Statistical Analysis of Failure Time Data , 2003, Technometrics.

[46]  R. Fine,et al.  Etiology and treatment of growth retardation in children with chronic kidney disease and end-stage renal disease: a historical perspective , 2010, Pediatric Nephrology.

[47]  Kevin V Lemley,et al.  National Kidney Foundation's Kidney Disease Outcomes Quality Initiative clinical practice guidelines for chronic kidney disease in children and adolescents: evaluation, classification, and stratification. , 2003, Pediatrics.