Application of pattern-mixture models to outcomes that are potentially missing not at random using pseudo maximum likelihood estimation

Biostatistics

Volumn 6, Issue 2, 2005, Pages 333-347

  Shen, Changyu ^a   Weissfeld, Lisa ^b  

^a INDIANA UNIVERSITY   (United States)

^b UNIVERSITY OF PITTSBURGH GRADUATE SCHOOL OF PUBLIC HEALTH   (United States)

Author keywords

Missing data; MNAR; Pattern mixture model; Pseudo maximum likelihood

Indexed keywords

AGED; ARTICLE; COMPARATIVE STUDY; COMPUTER SIMULATION; DEMENTIA; EDUCATIONAL STATUS; FEMALE; HUMAN; MALE; PATIENT; STATISTICAL MODEL; UNITED STATES;

AGED; COMPUTER SIMULATION; DEMENTIA; EDUCATIONAL STATUS; FEMALE; HUMANS; LIKELIHOOD FUNCTIONS; MALE; MODELS, STATISTICAL; PATIENT DROPOUTS; PENNSYLVANIA;

EID: 22144451514     PISSN: 14654644     EISSN: None     Source Type: Journal    
DOI: 10.1093/biostatistics/kxi013     Document Type: Article

References (26)

1. FITZMAURICE, G. M. AND LAIRD, N. M. (2000). Generalized linear mixture models for handling nonignorable dropouts in longitudinal studies. Biostatistics 1, 141-156.
2. FOLSTEIN, M. F., FOLSTEIN, S. E. AND MCHUGH, P. R. (1975). Mini-Mental State: a practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research 12, 189-198.
3. GANGULI, M., DODGE, H. H., CHEN, P., BELLE, S. AND DEKOSKY, S. T. (2000). Ten-year incidence of dementia in a rural elderly US community population: the MoVIES project. Neurology 54, 1109-1116.
4. GOURIEROUX, C., MONFORT, A. AND TROGNON, A. (1984). Pseudo maximum likelihood methods: theory. Econometricu 52, 681-700.
5. HAUSMAN, J. A. (1978). Specification tests in econometrics. Econometrica 46, 1251-1272.
6. HOGAN, J. W. AND LAIRD, N. M. (1997). Mixture models for the joint distribution of repeated measures and event times. Statistics in Medicine 16, 239-258.
7. IBRAHIM, J. G., CHEN, M. AND LIPSITZ, S. R. (2001). Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable. Biometrika 88, 551-564.
8. KENWARD, M. G., MOLENBERGHS, G. AND THUS, H. (2003). Pattern-mixture models with proper time dependence. Biometrika 90, 53-71.
9. KULLBACK, S. AND LEIBLER, R. A. (1951). On information and sufficiency. Annals of Mathematical Statistics 22, 79-86.
10. LIANG, K. Y. AND ZEGER, S. L. (1986). Longitudinal data analysis using generalized linear models. Biometrika 73, 13-22.
11. LIANG, K. Y., ZEGER, S. L. AND QUAQISH, B. (1992). Multivariate regression analysis for categorical data. Journal of the Royal Statistical Society, Series B 54, 3-40.
12. LIPSITZ, S. R., LAIRD, N. M. AND HARRINGTON, D. P. (1992). A three-stage estimator for studies with repeated and possibly missing binary outcomes. Applied Statistics 41, 203-213.
13. LITTLE, R. J. A. (1993). Pattern-mixture models for multivariate incomplete data. Journal of the American Statistical Association 88, 125-134.
14. LITTLE, R. J. A. (1994). A class of pattern-mixture models for normal incomplete data. Biometrika 81, 471-483.
15. LITTLE, R. J. A. (1995). Modeling drop-out mechanism in repeated-measures studies. Journal of the American Statistical Association 90, 1112-1121.
16. LITTLE, R. J. A. AND RUBIN, D. B. (1987). Statistical Analysis with Missing Data. New York: Wiley.
17. LITTLE, R. J. A. AND WANG, Y. (1996). Pattern-mixture models for multivariate incomplete data with covariates. Biometrics 52, 98-111.
18. PARK, T. AND LEE, S. (1999). Simple pattern-mixture models for longitudinal data with missing observations: analysis of urinary incontinence data. Statistics in Medicine 18, 2933-2941.
19. ROBINS, J. M. AND ROTNITZKY, A. (1995). Semiparametric efficiency in multivariate regression models with missing data. Journal of the American Statistical Association 90, 122-129.
20. ROTNITZKY, A., ROBINS, J. M. AND SCHARFSTEIN, D. O. (1998). Semiparametric regression for repeated outcomes with nonignorable nonresponse. Journal of the American Statistical Association 93, 1321-1339.
21. ROY, J. (2003). Modeling longitudinal data with nonignorable dropouts using a latent dropout class model. Biometrics 59, 829-836.
22. SHEN, C. (2004). Regression analysis in longitudinal analysis with non-ignorable missing outcomes, Ph.D. Dissertation, University of Pittsburgh.
23. THIJS, H., MOLENBERGHS, G., MICHIELS, B., VERBEKE, G. AND CURRAN, D. (2002). Strategies to fit pattern-mixture models. Biostatistics 3, 245-265.
24. WEDDERBURN, R. W. M. (1974). Quasi-likelihood functions, generalized linear models, and the Gauss-Newton method. Biometrika 61, 439-441.
25. WHITE, H. (1981). Consequences and detection of misspecified nonlinear regression models. Journal of the American Statistical Association 76, 419-433.
26. WHITE, H. (1982). Maximum likelihood estimation of misspecified models. Econometrica 50, 1-25.