Dynamic prediction using joint models of longitudinal and recurrent event data: a Bayesian perspective

In cardiovascular disease (CVD) studies, the events of interest may be recurrent (multiple occurrences from the same individual). During the study follow-up, longitudinal measurements are often ava...

[1]  C. Iadecola,et al.  Hypertension, Angiotensin, and Stroke: Beyond Blood Pressure , 2004, Stroke.

[2]  N. Poulter.  Beyond blood pressure. , 1994, Journal of human hypertension.

[3]  B. Davis,et al.  Major outcomes in high-risk hypertensive patients randomized to angiotensin-converting enzyme inhibitor or calcium channel blocker vs diuretic: The Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT). , 2002, JAMA.

[4]  R B Geskus,et al.  Dynamic prediction of recurrent events data by landmarking with application to a follow-up study of patients after kidney transplant , 2018, Statistical methods in medical research.

[5]  Fang Yao,et al.  FUNCTIONAL PRINCIPAL COMPONENT ANALYSIS FOR LONGITUDINAL AND SURVIVAL DATA , 2007 .

[6]  W. Gilks,et al.  Adaptive Rejection Metropolis Sampling Within Gibbs Sampling , 1995 .

[7]  J. Thayer,et al.  The relationship of autonomic imbalance, heart rate variability and cardiovascular disease risk factors. , 2010, International journal of cardiology.

[8]  Andrew Gelman,et al.  The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo , 2011, J. Mach. Learn. Res..

[9]  Jianwen Cai,et al.  Modelling recurrent events: a tutorial for analysis in epidemiology. , 2015, International journal of epidemiology.

[10]  Xuelin Huang,et al.  Joint analysis of correlated repeated measures and recurrent events processes in the presence of death, with application to a study on acquired immune deficiency syndrome , 2009 .

[11]  R. Kohn,et al.  Speeding Up MCMC by Efficient Data Subsampling , 2014, Journal of the American Statistical Association.

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

[13]  Cécile Proust-Lima,et al.  Quantifying and comparing dynamic predictive accuracy of joint models for longitudinal marker and time‐to‐event in presence of censoring and competing risks , 2015, Biometrics.

[14]  Virginie Rondeau,et al.  Joint model for left‐censored longitudinal data, recurrent events and terminal event: Predictive abilities of tumor burden for cancer evolution with application to the FFCD 2000–05 trial , 2016, Biometrics.

[15]  Cai Li,et al.  Fast covariance estimation for multivariate sparse functional data , 2018, Stat.

[16]  Joseph G Ibrahim,et al.  Bayesian Approaches to Joint Cure‐Rate and Longitudinal Models with Applications to Cancer Vaccine Trials , 2003, Biometrics.

[17]  Keith R. Abrams,et al.  Joint Modeling of Longitudinal and Survival Data , 2013 .

[18]  Gianfranco Parati,et al.  Predicting cardiovascular risk using conventional vs ambulatory blood pressure in older patients with systolic hypertension. , 1999 .

[19]  D. Vidt,et al.  Major cardiovascular events in hypertensive patients randomized to doxazosin versus chlorthalidone , 2000 .

[20]  Xiangyu Wang,et al.  Parallelizing MCMC with Random Partition Trees , 2015, NIPS.

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

[22]  Edsel A Peña,et al.  Parametric latent class joint model for a longitudinal biomarker and recurrent events , 2007, Statistics in medicine.

[23]  P. Heagerty,et al.  Survival Model Predictive Accuracy and ROC Curves , 2005, Biometrics.

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

[25]  Cécile Proust-Lima,et al.  Joint latent class models for longitudinal and time-to-event data: A review , 2014, Statistical methods in medical research.

[26]  Yee Whye Teh,et al.  Bayesian Learning via Stochastic Gradient Langevin Dynamics , 2011, ICML.

[27]  W. Kannel,et al.  Diabetes and cardiovascular disease. The Framingham study. , 1979, JAMA.

[28]  J. F. Lawless,et al.  Analysis of interval‐grouped recurrent‐event data using piecewise constant rate functions , 1998 .

[29]  W. Kannel.  Blood pressure as a cardiovascular risk factor: prevention and treatment. , 1996, JAMA.

[30]  T. Lumley,et al.  Time‐Dependent ROC Curves for Censored Survival Data and a Diagnostic Marker , 2000, Biometrics.

[31]  S. Greven,et al.  Multivariate Functional Principal Component Analysis for Data Observed on Different (Dimensional) Domains , 2015, 1509.02029.

[32]  G. Collins,et al.  Prediction models for cardiovascular disease risk in the general population: systematic review , 2016, British Medical Journal.

[33]  A. Avogaro,et al.  Recurrence of Cardiovascular Events in Patients With Type 2 Diabetes , 2008, Diabetes Care.

[34]  Francesca N. Delling,et al.  Heart Disease and Stroke Statistics—2018 Update: A Report From the American Heart Association , 2018, Circulation.