Sensitivity analysis for nonignorable missing responses with application to multivariate Random effect model
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A joint model with random effects for longitudinal mixed ordinal and continuous responses, with potentially non-random missing values in both types of responses is proposed. The presented model simultaneously considers a multivariate probit regression model for the missing mechanisms, which provides the ability of examining the missing data assumptions, and a multivariate mixed model for the responses. Random effects are used to take into account the correlation between longitudinal responses of the same individual. A full likelihood-based approach that allows yielding maximum likelihood estimates of the model parameters is used. The joint modeling of responses with the possibility of missing values requires caution since the interpretation of the fitted model highly depends on the assumptions that are unexaminable in a fundamental sense. A sensitivity of the results to the assumptions is also investigated. To illustrate the application of such modeling the longitudinal data of PIAT (Peabody Individual Achievement Test) is analyzed.
KeywordsLongitudinal studies Missing responses Mixed ordinal and continuous responses Random effect
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- Agresti, A. (2002) Categorical data and analysis, Wiely.Google Scholar
- Bahrami Samani, E. and Ganjali, M. (2008) A multivariate latent variable model for mixed continuous and ordinal responses, World Applied Sciences Journal, 3(2), 294–299.Google Scholar
- Bahrami Samani, E., Ganjali, M. and Eftekhari Mahabadi, S. (2010) A latent variable model for mixed continuous and ordinal responses with nonignorable missing responses, Sankhya, 72-B, 38–57.Google Scholar
- Baker, P. C., Keck, C. K., Mott, F. L. and Quinlan, S. V. (1993) NLSY child handbook: guide to the 1986–1990 national longitudinal survey of youth child data, Columbus, OH: Center for Human Resource Research.Google Scholar
- Catalano, P. and Ryan, L. M. (1992) Bivariate latent variable models for clustered discrete and continuous outcoms, Journal of the American Statistical Association, 50(3), 1078–1095.Google Scholar
- Diggle, P. J., Heagerty, P., Liang, K. Y. and Zeger, S. L. (2002) Analysis of longitudinal data, Oxford: University Press.Google Scholar
- Ganjali, M. and Shafie, K. (2006) A transition model for an ordered cluster of mixed continuous and discrete responses with non-monotone missingness, Journal of Applied Statistical sciences; Volume 15 Issue 3.Google Scholar
- Kaciroti, N. A., Raghunathan, T. E., Schork, M. A., Clark, N. M. and Gong, M. (2006) A Bayesian Approach for Clustered Longitudinal Ordinal Outcome With Nonignorable Missing Data: Evaluation of an Asthma Education Program, Journal of the American Statistical Association, 474, 435–446.MathSciNetCrossRefGoogle Scholar
- Mcculloch, C. (2007) Joint modelling of mixed outcome type using latend variables, statistical methods in Medical Research, 1–27.Google Scholar
- Rubin, D. B. (1976) Inference and missing data, Biometrica, 82, 669–710.Google Scholar
- Verbeke, G. and Molenberghs, G. (1997) Linear mixed models in practice: A SAS Oriented Approach, Springer.Google Scholar