Jointly modeling time-to-event and longitudinal data: a Bayesian approach

详细信息    查看全文

• 作者：Yangxin Huang (1)
  X. Joan Hu (2)
  Getachew A. Dagne (1)
  
• 关键词：Accelerated failure time model ; Dirichlet process ; Semiparametric linear/nonlinear mixed ; effects model ; Skew ; elliptical distribution ; Time ; to ; event
• 刊名：Statistical Methods & Applications
• 出版年：2014
• 出版时间：March 2014
• 年：2014
• 卷：23
• 期：1
• 页码：95-121
• 全文大小：571 KB
• 参考文献：1. Acosta EP, Wu H, Walawander A, Eron J, Pettinelli C, Yu S, Neath D, Ferguson E, Saah AJ, Kuritzkes DR, Gerber JG (2004) Comparison of two indinavir/ritonavir regimens in treatment-experienced HIV-infected individuals. J Acquir Immune Defic Syndr 37:1358鈥?366 CrossRef
  2. Arellano-Valle RB, Genton MG (2005) On fundamental skew distributions. J Multivar Anal 96:93鈥?16 CrossRef
  3. Arellano-Valle RB, Bolfarine H, Lachos VH (2007) Bayesian inference for skew-normal linear mixed models. J Appl Stat 34:663鈥?82 CrossRef
  4. Azzalini A, Capitanio A (2003) Distributions generated by perturbation of symmetry with emphasis on a multivariate skew $t$ distributions. J Roy Stat Soc Ser B 65:367鈥?89 CrossRef
  5. Brooks SP, Gelman A (1998) General methods for monitoring convergence of iterative simulations. J Comput Graph Stat 7(4):434鈥?55
  6. Carroll RJ, Ruppert D, Stefanski LA, Crainiceanu CM (2006) Meas Error Nonlinear Models Mod Persp, 2nd edn. Chapman and Hall, London CrossRef
  7. DeGruttola V, Tu XM (1994) Modeling progression of CD4-lymphocyte count and its relationship to survival time. Biometrics 50:1003鈥?014 CrossRef
  8. Ferguson TS (1973) A Bayesian analysis of some nonparametric problems. Ann Stat 1:209鈥?30 CrossRef
  9. Gelman A, Rubin DB (1992) Inference from iterative simulation using multiple sequences. Stat Sci 7(4):457鈥?11 CrossRef
  10. Gelman A (2006) Prior distributions for variance parameters in hierarchical models. Bayesian Anal 1:515鈥?33 CrossRef
  11. Hougaard P (1999) Fundamentals of survival data. Biometrics 55:13鈥?2 CrossRef
  12. Huang Y, Liu D, Wu H (2006) Hierarchical Bayesian methods for estimation of parameters in a longitudinal HIV dynamic system. Biometrics 62:413鈥?23 CrossRef
  13. Huang Y, Dagne G (2011) A Bayesian approach to joint mixed-effects models with a skew-normal distribution and measurement errors in covariates. Biometrics 67:260鈥?69 CrossRef
  14. Huang Y, Dagne G, Wu L (2011) Bayesian inference on joint models of HIV dynamics for time-to-event and longitudinal data with skewness and covariate measurement errors. Stat Med 30:2930鈥?946 CrossRef
  15. Ishwaran H, James L (2002) Dirichlet process computing in finite normal mixtures: smoothing and prior information. J Comput Graph Stat 11:508鈥?32 CrossRef
  16. Liu W, Wu L (2007) Simultaneous inference for semiparametric nonlinear mixed-effects models with covariate measurement errors and missing responses. Biometrics 63:342鈥?50 CrossRef
  17. Lunn DJ, Thomas A, Best N, Spiegelhalter D (2000) WinBUGS: a Bayesian modelling framework: concepts, structure, an extensibility. Stat Comput 10:325鈥?37 CrossRef
  18. Ntzoufras I (2009) Bayesian modeling using WinBUGS. Wiley, New Jersey CrossRef
  19. Pawitan Y, Self S (1993) Modeling disease marker processes in AIDS. J Am Stat Assoc 88:719鈥?26
  20. Rubin DB (1976) Inference and missing data. Biometrika 63:581鈥?92 CrossRef
  21. Sahu SK, Dey DK, Branco MD (2003) A new class of multivariate skew distributions with applications to Bayesian regression models. Can J Stat 31:129鈥?50 CrossRef
  22. Tsiatis AA, Davidian M (2004) Joint modeling of longitudinal and time-to-event data: an overview. Stat Sin 14:809鈥?34
  23. Wu H, Zhang J-T (2002) The study of long-term HIV dynamics using semi-parametric non-linear mixed-effects models. Stat Med 21:3655鈥?675 CrossRef
  24. Wu H, Ding AA (1999) Population HIV-1 dynamics in vivo: applicable models and inferential tools for virological data from AIDS clinical trials. Biometrics 55:410鈥?18 CrossRef
  25. Wu L (2009) Mixed effects model for complex data. Chapman and Hall, London CrossRef
  26. Wu L, Liu W, Hu XJ (2010) Joint inference on HIV viral dynamics and immune suppression in presence of measurement errors. Biometrics 66:327鈥?35 CrossRef
  
• 作者单位：Yangxin Huang (1)
  X. Joan Hu (2)
  Getachew A. Dagne (1)
  
  1. Department of Epidemiology and Biostatistics, College of Public Health, MDC 56, University of South Florida, Tampa, FL, 33612, USA
  2. Department of Statistics and Actuarial Sciences, Simon Fraser University, Burnaby, BC, V5A聽1S6, Canada
  
• ISSN：1613-981X

文摘

This article explores Bayesian joint models of event times and longitudinal measures with an attempt to overcome departures from normality of the longitudinal response, measurement errors, and shortages of confidence in specifying a parametric time-to-event model. We allow the longitudinal response to have a skew distribution in the presence of measurement errors, and assume the time-to-event variable to have a nonparametric prior distribution. Posterior distributions of the parameters are attained simultaneously for inference based on Bayesian approach. An example from a recent AIDS clinical trial illustrates the methodology by jointly modeling the viral dynamics and the time to decrease in CD4/CD8 ratio in the presence of CD4 counts with measurement errors to compare potential models with various scenarios and different distribution specifications. The analysis outcome indicates that the time-varying CD4 covariate is closely related to the first-phase viral decay rate, but the time to CD4/CD8 decrease is not highly associated with either the two viral decay rates or the CD4 changing rate over time. These findings may provide some quantitative guidance to better understand the relationship of the virological and immunological responses to antiretroviral treatments.