Mixed Correlated Bivariate Ordinal and Negative Binomial Longitudinal Responses with Nonignorable Missing Values

References

• Agresti, A. (2002). Categorical Data and Analysis. New York: Wiley.
   Google Scholar
• Anderson, T.W., Goodman, LA (1957). Statistical inference about Markov chains. Ann. Math. Statist. 28:89110.
   Google Scholar
• Berridge, D.M., Santos, D.M. (1996). Fitting a random effects model to ordinal recurrent events using existing software. J. Statist. Comput. Simul. 55:7386.
   Web of Science ®Google Scholar
• Catalano, P., Ryan, L.M. (1992). Bivariate latent variable models for clustered discrete and continuous outcoms. J. Amer. Statist. Assoc. 50(3): 10781095.
   Google Scholar
• Cameron, A.C., Trivedi, P.K. (1998). Regression Analysis of Count Data. Cambridge: Cambridge University Press.
   Google Scholar
• Cox, D.R., Wermuth, N. (1992). Response models for mixed binary and quantitative variables. Biometrika 79(3): 441461.
   Web of Science ®Google Scholar
• Cook, R.D. (1986). Assessment of Local Influence (with discussion). J. Roy. Statist. Soc. Ser. B. 48:133169.
   Google Scholar
• Diggle, P.J., Heagerty, P., Liang, K.Y., Zeger, S.L. (2002). Analysis of Longitudinal Data. Oxford: Oxford University Press.
   Google Scholar
• Diggle, P.J., Kenward, M.G. (1994). Informative drop-out in longitudinal data analysis. J. Appl. Statist. 43:4993.
   Web of Science ®Google Scholar
• Dunson, D.B. (2000). Bayesian latent variable models for clustered mixed outcomes. J. Roy. Statist. Soc. Ser. B Statist. Methodol. 62(2): 355366.
   Web of Science ®Google Scholar
• Fahrmeir, L., Tutz, G. (2001). Multivariate Statistical Modelling based on Generalized Linear Models. New York: Springer.
   Google Scholar
• Fitzmaurce, G.M., Laird, N.M. (1995). Regression models for bivariate discrete and continuous outcome with clustering. J. Amer. Statist. Associat. 90:845852.
   Web of Science ®Google Scholar
• Fletcher, R. (2000). Practical Methods of Optimization. New York: John Wiley.
   Google Scholar
• Kaciroti, N.A., Raghunathan, T.E., Schork, M.A., Clark, N.M., Gong, M. (2006). A Bayesian approach for clustered longitudinal ordinal outcome with nonignorable missing data: evaluation of an asthma education program. J. Amer. Statist. Associat. 474:435446.
   Web of Science ®Google Scholar
• Harvile, D.A., Mee, R.W. (1984). A mixed model procedure for analyzing ordered categorical data. Biometrics 40:393408.
   Web of Science ®Google Scholar
• Liang, K.Y., Zeger, S.L., Qaqish, B.F. (1992). Multivariate regression analyzes for categorical data (with discussion). J. Roy. Statist. Soc. B 54:340.
   Google Scholar
• Lin, X., Ryan, L., Sammel, M., Zhang, D., Padungtod, C., Xu, X. (2000). A scaled linear mixed model for multiple outcomes. Biometrics 56(2): 593601.
   PubMed Web of Science ®Google Scholar
• Little, R.J., (1987). Maximum likelihood estimation for mixed continuous and categorical data with missing values. Biometrika 72:497512.
   Web of Science ®Google Scholar
• McCullagh, P. (1980). Regression models for ordinal data (with discussion). J. Roy. Statist. Soc. B 42:109142.
   Google Scholar
• McCulloch, C. (2007). Joint modelling of mixed outcome type using latend variables. Statist. Meth. Med. Res. 17: 5373.
   PubMed Web of Science ®Google Scholar
• Sammel, M.D., Ryan, L.M., Legler, J.M. (1997). Latent variable models for mixed discrete and continuous outcomes. J. Roy. Statist. Soc. Ser. B Meth. 59:667678.
   Google Scholar
• Sengul, T.K., Stoffer, D.S., Day, N.L. (2007). A residuals-based transition model for longitudinal analysis with estimation in the presence of missing data. Statist. Med. 26:33303341.
   PubMed Web of Science ®Google Scholar
• Stiratelli, R., Laird, N., Ware, J.H. (1984). Random-effect models for serial observations with binary response. Biometrics 40:961971.
   PubMed Web of Science ®Google Scholar
• Teixeira-Pinto, A., Normand, S. T. (2009). Correlated bivariate continuous and binary outcomes: issues and applications. Statist. Med. 28(13): 17531773.
   PubMed Web of Science ®Google Scholar
• Tutz, G. (2005). Modeling of repeated ordered measurements by isotonic sequential regression. Statist. Mod. 5:269287.
   Web of Science ®Google Scholar
• Verbeke, G., Molenberghs, G. (1997). Linear Mixed Models in Practice: A SAS Oriented Approach. New York: Springer.
   Google Scholar
• Yang, Y., Kang, J. (2011). Joint analysis of mixed Poisson and continuous longitudinal data with nonignorable missing values. Computat. Statist. Data Anal. 54:193207.
   Web of Science ®Google Scholar