Modeling Longitudinal-Competing Risks Data With Skew Distribution and Mismeasured Covariate

References

• Arellano-Valle, R., and Genton, M. (2005), “On Fundamental Skew Distributions,” Journal of Multivariate Analysis, 96, 93–116.
   Web of Science ®Google Scholar
• Azzalini, A., and Capitanio, A. (2003), “On Fundamental Skew Distributions,” Journal of Royal Statistical Society, Series B, 65, 367–389.
   Google Scholar
• Azzalini, A., and Genton, M. (2008), “Robust Likelihood Methods Based on the Skew-t and Related Distributions,” International Statistical Review, 76, 106–129.
   Web of Science ®Google Scholar
• Elashoff, R. M., Li, G., and Li, N. (2008), “A Joint Model for Longitudinal Measurements and Survival Data in the Presence of Multiple Failure Types,” Biometrics, 64, 762–771.
   PubMed Web of Science ®Google Scholar
• Eubank, R. (1999), Nonparametric Regression and Spline Smoothing, New York: Dekker.
   Google Scholar
• Gelman, A., Carlin, J., Stern, H., and Rubin, D. (2003), Bayesian Data Analysis, London: Chapman and Hall.
   Google Scholar
• Gelman, A., and Rubin, D. (1992), “Inference from iterative simulation using multiple sequences,” Statistical Science, 7, 457–511.
   Google Scholar
• Ho, H., and Lin, T. (2010), “Robust Linear Mixed Models Using the Skew-t Distribution With Application to Schizophrenia Data,” Biometrical Journal, 52, 449–469.
   PubMed Web of Science ®Google Scholar
• Hu, W., Li, G., and Li, N. (2009), “A Bayesian Approach to Joint Analysis of Longitudinal Measurements and Competing Risks Failure Time Data,” Statistics in Medicine, 28, 1601–1619.
   PubMed Web of Science ®Google Scholar
• Huang, Y., Dagne, G., and Wu, L. (2011), “Bayesian Inference on Joint Models of HIV Dynamics for Time-to-Event and Longitudinal Data With Skewness and Covariate Measurement Errors,” Statistics in Medicine, 30, 2930–2946.
   PubMed Web of Science ®Google Scholar
• Huang, Y., Hu, X., and Dagne, G. (2014), “Jointly Modeling Time-to-Event and Longitudinal Data: A Bayesian Approach,” Statistical Methods & Applications, 23, 95–121.
   PubMed Web of Science ®Google Scholar
• Jara, A., Quintana, F., and Martin, E. (2008), “Linear Mixed Models With Skew-Elliptical Distributions: A Bayesian Approach,” Computational Statistics and Data Analysis, 52, 5033–5045.
   Web of Science ®Google Scholar
• Lachos, V., Bandyopadhyay, D., and Dey, D. (2011), “Linear and Nonlinear Mixed-Effects Models for Censored HIV Viral Loads Using Normal/Independent Distributions,” Biometrics, 67, 1594–1604.
   PubMed Web of Science ®Google Scholar
• Li, Y., Wileyto, E. P., and Heitjan, D. F. (2010), “Modeling Smoking Cessation Data With Alternating States and a Cure Fraction Using Frailty Models,” Statistics in Medicine, 29, 627–638.
   PubMed Web of Science ®Google Scholar
• Liang, H., Wu, H., and Carroll, R. (2003), “The Relationship Between Virologic and Immunologic Responses in AIDS Clinical Research Using Mixed-Effects Varying-Coefficient Models With Measurement Error,” Biostatistics, 4, 297–312.
   PubMed Web of Science ®Google Scholar
• Liu, W., and Wu, L. (2007), “Simultaneous Inference for Semiparametric Nonlinear Mixed-Effects Models With Covariate Measurement Errors and Missing Responses,” Biometrics, 63, 342–350.
   PubMed Web of Science ®Google Scholar
• Lu, T., and Huang, Y. (2015), “Bayesian Inference on Mixed-Effects Varying-Coefficient Joint Models With ST Distribution for Longitudinal Data with Multiple Features,” Statistical Methods in Medical Research, doi: 10.1177/0962280215569294.
   Google Scholar
• Luo, S. (2014), “A Bayesian Approach to Joint Analysis of Multivariate Longitudinal Data and Parametric Accelerated Failure Time,” Statistics in Medicine, 33, 580–594.
   PubMed Web of Science ®Google Scholar
• Luo, S., Su, X., Yi, M., and Hunt, K.K. (2015), “Simultaneous Inference of a Misclassified Outcome and Competing Risks Failure Time Data,” Journal of Applied Statistics, 42, 1080–1090.
   PubMed Web of Science ®Google Scholar
• Padierna-Olivos, L., Moreno-Altamirano, M., Sanchez-Colon, S., Masso-Rojas, F., and Sanchez-Garcia, F. (2000), “Connectivity and HIV-1 Infection: Role of CD4(+) T-cell Counts and HIV-1 RNA Copy Number,” Scandinavian Journal of Immunology, 52, 618–627.
   PubMed Web of Science ®Google Scholar
• Sahu, S., Dey, D., and Branco, M. (2003), “A New Class of Multivariate Skew Distributions With Applications to Bayesian Regression Models,” The Canadian Journal of Statistics, 31, 129–150.
   Web of Science ®Google Scholar
• Spiegelhalter, D., Best, N., Carlin, B., and Van der Linde, A. (2002), “Bayesian Measures of Model Complexity and Fit,” Journal of the Royal Statistical Society, Series B, 64, 583–639.
   Google Scholar
• Wu, L. (2002), “A Joint Model for Nonlinear Mixed-Effects Models With Censoring and Covariates Measured With Error,” Journal of the American Statistical Association, 97, 955–964.
   Web of Science ®Google Scholar
• Wu, L., Hu, X. J., and Wu, H. (2008), “Joint Inference for Nonlinear Mixed-Effects Models and Time-to-Event at the Presence of Missing Data,” Biostatistics, 9, 308–320.
   PubMed Web of Science ®Google Scholar
• Wu, L., Liu, W., and Hu, X. J. (2010), “Joint Inference on HIV Viral Dynamics and Immune Suppression in Presence of Measurement Errors,” Biometrics, 66, 327–335.
   PubMed Web of Science ®Google Scholar
• Yu, B., and Ghosh, P. (2010), “Joint Modeling for Cognitive Trajectory and Risk of Dementia in the Presence of Death,” Biometrics, 66, 294–300.
   PubMed Web of Science ®Google Scholar