Bayesian bivariate bent-cable model for longitudinal data

References

• Acosta, E. P., H. Wu, A. Walawander, J. J. Eron, C. J. Fichtenbaum, C. Pettinelli, D. Neath, E. Ferguson, A. J. Saah, and J. G. Gerber. 2004. Comparison of two indinavir/ritonavir regimens in treatment-experienced HIV-infected individuals. Journal of Acquired Immune Deficiency Syndromes 37:1358–66.
   PubMed Web of Science ®Google Scholar
• Bandyopadhyay, D., V. Lachos, C. Abanto-Valle, and P. Ghosh. 2010. Linear mixed models for skew-normal/independent bivariate responses with an application to periodontal disease. Statistics in Medicine 29 (25):2643–55. doi:10.1002/sim.4031.
   PubMed Web of Science ®Google Scholar
• Chiu, G., R. Lockhart, and R. Routledge. 2006. Bent-cable regression theory and applications. Journal of the American Statistical Association 101 (474):542–53. doi:10.1198/016214505000001177.
   Web of Science ®Google Scholar
• Chiu, G, and R. Lockhart. 2010. Bent-cable regression with autoregressive noise. Canadian Journal of Statistics 38 (3):386–407. doi:10.1002/cjs.10070.
   Web of Science ®Google Scholar
• Demirhan, H, and C. Hamurkaroglu. 2011. On a multivariate log-gamma distribution and the use of the distribution in the Bayesian analysis. Journal of Statistical Planning and Inference 141 (3):1141–52. doi:10.1016/j.jspi.2010.09.015.
   Web of Science ®Google Scholar
• Duncan, T. E. S. C. Duncan, and L. A. Strycker. 2006. An introduction to latent variable growth curve modeling: Concepts, issues, and applications (2nd ed.). Mahwah, NJ: Erlbaum,
   Google Scholar
• Genton, M. G. 2004. Skew-elliptical distributions and their applications: A journey beyond normality, Edited Volume, Boca Raton: Chapman & Hall/CRC.
   Google Scholar
• Ghosh, P., M. D. Branco, and H. Chakraborty. 2007. Bivariate random effect model using skew-normal distribution with application to HIV-RNA. Statistics in Medicine 26 (6):1255–67. doi:10.1002/sim.2667.
   PubMed Web of Science ®Google Scholar
• Hall, C., J. Ying, L. Kuo, and R. Lipton. 2003. Bayesian and profile likelihood changepoint methods for modeling cognitive function over time. Computational Statistics & Data Analysis 42 (1-2):91–109. doi:10.1016/S0167-9473(02)00148-2.
   Web of Science ®Google Scholar
• Khan, S. A., G. Chiu, and J. A. Dubin. 2009. Atmospheric concentration of chloroflurocarbons: Addressing the global concern with the longitudinal bent-cable model. Chance 22 (3):8–17. doi:10.1080/09332480.2009.10722965.
   Google Scholar
• Kiuchi, A. S., J. A. Hartigan, T. R. Holford, P. Rubinstein, and C. E. Stevens. 1995. Change Points in the Series of T4 Counts Prior to AIDS. Biometrics 51 (1):236–48. doi:10.2307/2533329.
   PubMed Web of Science ®Google Scholar
• Lunn, D., A. Thomas, N. Best, and D. Spiegelhalter. 2000. WinBUGS – a Bayesian modelling framework: Concepts, structure, an extensibility. Statistics and Computing 10 (4):325–37. doi:10.1023/A:1008929526011.
   Web of Science ®Google Scholar
• Muthen, B. O, and P. J. Curra. 1997. General Longitudinal Modeling of Individual Differences in Experimental Designs: A Latent Variable Framework for Analysis and Power Estimation. Psychological Methods 2 (4):371–402.
   Web of Science ®Google Scholar
• Ntzoufras, I. 2009. Bayesian Modeling Using WinBUGS. Wiley: New Jersey: Wiley,
   Google Scholar
• Paterson, D., S. Swindells, J. Mohr, M. Brester, E. Vergis, C. Squier, M. Wagener, and N. Singh. 2000. Adherence to protease inhibitor therapy and outcomes in patients with HIV infection. Annals of Internal Medicine 133 (1):21–30. doi:10.7326/0003-4819-133-1-200007040-00004.
   PubMed Web of Science ®Google Scholar
• Pinheiro, J. C., and D. M. Bates. 2000. Mixed-Effects Models in S and SPLUS. New York: Springer.
   Google Scholar
• Sahu, S. K., D. K. Dey, and M. D. Branco. 2003. A new class of multivariate skew distributions with applications to Bayesian regression models. Canadian Journal of Statistics 31 (2):129–50. doi:10.2307/3316064.
   Web of Science ®Google Scholar
• Sammel, M., X. Lin, and L. Ryan. 1999. Multivariate linear mixed models for multiple outcomes. Statistics in Medicine 18 (17-18):2479–92. doi:10.1002/(SICI)1097-0258(19990915/30)18:17/18<2479::AID-SIM270>3.0.CO;2-F.
   PubMed Web of Science ®Google Scholar
• Shah, A., N. Laird, and D. Schoenfeld. 1997. A random-effects model for multiple characteristics with possibly missing data. Journal of the American Statistical Association 92 (438):775–9. doi:10.1080/01621459.1997.10474030.
   Web of Science ®Google Scholar
• Slate, E, and L. Lark. 2001. Using PSA to detect prostate cancer onset: An application of Bayesian retrospective and prospective changepoint identification. Journal of Educational and Behavioral Statistics 26:443468.
   Google Scholar
• Lin, T. I., and W. L. Wang. 2013. Multivariate skew-normal at linear mixed models for multi-outcome longitudinal data. Statistical Modelling 13 (3):199–221. doi:10.1177/1471082X13480283.
   Web of Science ®Google Scholar
• Zhao, L., D. Feng, B. Neelon, and M. Buyse. 2015. Evaluation of treatment efficacy using a Bayesian mixture piecewise linear model of longitudinal biomarkers. Statistics in Medicine 34 (10):1733–46. doi:10.1002/sim.6445.
   PubMed Web of Science ®Google Scholar