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Article

Analysis of longitudinal ordinal data using semi-parametric mixed model under missingness

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Pages 5631-5642 | Received 20 Apr 2019, Accepted 30 May 2020, Published online: 07 Jul 2020

References

  • Bacci, S., F. Bartolucci, and S. Pandolfi. 2018 . A joint model for longitudinal and survival data based on an AR(1) latent process. Statistical Methods in Medical Research 27 (5):1285–311. doi:10.1177/0962280216659895.
  • Chen, B., G. Y. Yi, and R. J. Cook. 2010. Weighted generalized estimating functions for longitudinal response and covariate data that are missing at random. Journal of the American Statistical Association 105 (489):336–53. doi:10.1198/jasa.2010.tm08551.
  • Chib, S., and E. Greenberg. 1995. Understanding the metropolis-hastings algorithm. The American Statistician 49 (4):327–35. doi:10.1080/00031305.1995.10476177.
  • Das, K., S. Roy, and A. K. Chattopadhyay. 2016. Analysis of ordinal longitudinal data using semi-parametric mixed models. Journal of Statistical Research 48–50:15–33.
  • Durban, M., J. Harezlak, M. P. Wand, and R. J. Carroll. 2005. Simple fitting of subject-specific curves for longitudinal data. Statistics in Medicine 24 (8):1153–67. doi:10.1002/sim.1991.
  • Jacqmin-Gadda, H., C. Proust-Lima, and H. Amieva. 2010. Semi-parametric latent process model for longitudinal ordinal data: Application to cognitive decline. Statistics in Medicine 29 (26):2723–31. doi:10.1002/sim.4035.
  • He, X., Z. Y. Zhu, and W. K. Fung. 2002. Estimation in a semiparametric model for longitudinal data with unspecified dependence structure. Biometrika 89 (3):579–90. doi:10.1093/biomet/89.3.579.
  • Horton, H. J., and N. M. Laird. 1999. Maximum likelihood analysis of generalized linear models with missing covariates. Statistical Methods in Medical Research 8 (1):37–50. doi:10.1177/096228029900800104.
  • Ibrahim, J. G., S. R. Lipsitz, and M. H. Chen. 1999. Missing covariates in generalized linear models when the missing data mechanism is non-ignorable. Journal of the Royal Statistical Society: Series B 61 (1):173–90. doi:10.1111/1467-9868.00170.
  • Ibrahim, J. G., M. H. Chen, and S. R. Lipsitz. 2001. Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable. Biometrika 88 (2):551–64. doi:10.1093/biomet/88.2.551.
  • Lee, K., and M. J. Daniels. 2007. A class of Markov models for longitudinal ordinal data. Biometrics 63 (4):1060–67. doi:10.1111/j.1541-0420.2007.00800.x.
  • Lee, K., and M. J. Daniels. 2008 . Marginalized models for longitudinal ordinal data with application to quality of life studies. Statistics in Medicine 27 (21):4359–80. doi:10.1002/sim.3352.
  • Li, C. S. 2011. A lack-of-fit test for parametric zero-inflated poisson models. Journal of Statistical Computation and Simulation 81 (9):1081–98. doi:10.1080/00949651003677410.
  • Lin, X., and R. J. Carroll. 2001. Semiparametric regression for clustered data using generalized estimating equations. Journal of the American Statistical Association 96 (455):1045–56. doi:10.1198/016214501753208708.
  • Lipsitz, S. R., J. G. Ibrahim, and L. P. Zhao. 1999. A weighted estimating equation for missing covariate data with properties similar to maximum likelihood. Journal of the American Statistical Association 94 (448):1147–60. doi:10.1080/01621459.1999.10473870.
  • Liu, L. C., and D. Hedeker. 2006 . A mixed-effects regression model for longitudinal multivariate ordinal data. Biometrics 62 (1):261–68. doi:10.1111/j.1541-0420.2005.00408.x.
  • McCulloch, C. E. 1997. Maximum likelihood algorithms for generalized linear mixed models. Journal of the American Statistical Association 92 (437):162–70. doi:10.1080/01621459.1997.10473613.
  • O’Bryant. S. E. 2008. Staging dementia using clinical dementia rating scale sum of boxes scores. Archives of Neurology 65 (8):1091–95. doi:10.1001/archneur.65.8.1091.
  • Rizopoulos, D. 2012. Joint models for longitudinal and time-to-event data: With applications in R. Boca Raton, FL: CRC Press.
  • Stubbendick, A. L., and J. G. Ibrahim. 2003. Maximum likelihood methods for nonignorable missing responses and covariates in random effects models. Biometrics 59 (4):1140–50. doi:10.1111/j.0006-341x.2003.00131.x.
  • Stubbendick, A. L., and J. G. Ibrahim. 2006. Likelihood-based inference with nonignorable missing responses and covariates in models for discrete longitudinal data. Statistica Sinica 16:1143–67.
  • Troxel, A. B., S. R. Lipsitz, and T. A. Brennan. 1997. Weighted estimating equations with nonignorably missing response data. Biometrics 53 (3):857–69. doi:10.2307/2533548.
  • Zeger, S. L., and P. J. Diggle. 1994. Semiparametric models for longitudinal data with application to CD4 cell numbers in HIV seroconverters. Biometrics 50 (3):689–99. doi:10.2307/2532783.
  • Zhang, D., X. Lin, J. Raz, and M. Sowers. 1998. Semiparametric stochastic mixed models for longitudinal data. Journal of the American Statistical Association 93 (442):710–19. doi:10.1080/01621459.1998.10473723.

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