Sensitivity analysis of mixed models for incomplete longitudinal data

Journal of Educational and Behavioral Statistics

Volumn 36, Issue 2, 2011, Pages 237-256

  Xu, Shu ^a   Blozis, Shelley A ^b  

^a PENNSYLVANIA STATE UNIVERSITY   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

data analysis; hierarchical modeling; longitudinal studies

Indexed keywords

EID: 79955069622     PISSN: 10769986     EISSN: None     Source Type: Journal    
DOI: 10.3102/1076998610375836     Document Type: Article

References (29)

1. Blozis, S.A. (2004). Structured latent curve models for the study of change in multivariate repeated measures. Psychological Methods, 9, 334-353.
2. Blozis, S.A. (2007). A Newton procedure for a conditionally linear mixed-effects model. Behavior Research Methods, 39, 695-708.
3. Blozis, S.A., & Cudeck, R. (1999). Conditionally linear mixed-effects models with latent variable covariates. Journal of Educational and Behavioral Statistics, 24, 245-270.
4. Browne, M. (1993). Structured latent curve models. In C. M. Cuadras & C. R. Rao (Eds.), Multivariate analysis: Future directions 2. Amsterdam, Netherlands: Elsevier Science Publishers B. V.
5. Browne, M.W., & Du Toit, S.H.C. (1991). Models for learning data. In L. M. Collins & J. L. Horn (Eds.), Best methods for the analysis of change (pp. 47-68). Washington, DC: American Psychological Association.
6. Collins, L.M., Schafer, J.L., & Kam, C.-H. (2001). A comparison of inclusive and restrictive strategies in modern missing data procedures. Psychological Methods, 6, 330-351.
7. Cudeck, R., & Du Toit, S.H.C. (2002). A version of quadratic regression with interpretable parameters. Multivariate Behavioral Research, 37, 501-519.
8. Davidian, W., & Giltinan, D.M. (1995). Nonlinear models for repeated measurement data. London, England: Chapman & Hall.
9. Diggle, P.J., & Kenward, M.G. (1994). Informative drop-out in longitudinal data analysis (with discussion). Applied Statistics, 43, 49-93.
10. Dmitrienko, A., Offen, W.W., Faries, D., Chuang-Stein, C., & Molenberghs, G. (2005). Analysis of clinical trial data using the SAS System. Cary, NC: SAS Publishing.
11. Graham, J.W. (2003). Adding missing-data-relevant variables to FIML-based structural equation models. Structural Equation Modeling, 10, 80-100.
12. Harring, J.R., Cudeck, R., & Du Toit, S.H.C. (2006). Fitting partially nonlinear random coefficient models as SEMs. Multivariate Behavioral Research, 41, 579-596.
13. Heckman, J.I. (1976). The common structure of statistical models of truncation, sample selection and limited dependent variables, and a simple estimator for such models. Annals of Economic and Social Measurement, 5, 475-492.
14. Hedeker, D., & Gibbons, R.D. (1997). Application of random-effects pattern-mixture models for missing data in longitudinal studies. Psychological Methods, 2, 64-78.
15. Hogan, J.W., & Laird, N.M. (1997). Mixture models for the joint distribution of repeated measures and event times. Statistics in Medicine, 16, 239-257.
16. Jansen, I., Hens, N., Molenberghs, G., Aerts, M., Verbeke, G., & Kenward, M.G. (2006). The nature of sensitivity in missing not at random models. Computational Statistics and Data Analysis, 50, 830-858.
17. Laird, N.M. (1988). Missing data in longitudinal studies. Statistics in Medicine, 7, 305-315.
18. Little, R.J.A. (1993). Pattern-mixture models for multivariate incomplete data. Journal of the American Statistical Association, 88, 125-133.
19. Little, R.J.A. (1994). A class of pattern-mixture models for normal incomplete data. Biometrika, 81, 471-483.
20. Little, R.J.A. (1995). Modeling the drop-out mechanism in repeated measures studies. Journal of the American Statistical Association, 90, 1112-1121.
21. Little, R.J.A., & Rubin, D.B. (2002). Statistical analysis with missing data (2nd ed.). New York, NY: John Wiley.
22. Meredith, W., & Tisak, J. (1990). Latent curve analysis. Psychometrika, 55, 107-122.
23. Molenberghs, G., Beunckens, C., Sotto, C., & Kenward, M.G. (2008). Every missing not at random model has got a missing at random counterpart with equal fit. Journal of the Royal Statistical Society Series B, 70, 371-388.
24. Molenberghs, G., & Kenward, M.G. (2007). Missing data in clinical studies. West Sussex, England: John Wiley.
25. Rubin, D.B. (1987). Multiple imputation for nonresponse in surveys. New York, NY: John Wiley.
26. Schafer, J.L., & Graham, J.W. (2002). Missing data: Our view of the state of the art. Psychological Methods, 7, 147-177.
27. Singer, J.D., & Willett, J. (2003). Applied longitudinal data analysis: Modeling change and event occurrence. New York, NY: Oxford University Press.
28. Willett, J., & Sayer, A. (1994). Using covariance structure analysis to detect correlates and predictors of individual change over time. Psychological Bulletin, 116, 363-380.
29. Wu, M.C., & Carroll, R.J. (1988). Estimation and comparison of changes in the presence of informative right censoring by modeling the censoring process. Biometrics, 44, 175-188.