Missing covariates in generalized linear models when the missing data mechanism is non-ignorable

Journal of the Royal Statistical Society. Series B: Statistical Methodology

Volumn 61, Issue 1, 1999, Pages 173-190

  Ibrahim, Joseph G ^a   Lipsitz, Stuart R ^b   Chen, Ming Hui ^b  

^a DANA FARBER CANCER INSTITUTE   (United States)

^b NONE

Author keywords

EM algorithm; Gibbs sampling; Logistic regression; Maximum likelihood estimation; Missing data mechanism; Monte Carlo EM algorithm

Indexed keywords

EID: 0033474233     PISSN: 13697412     EISSN: None     Source Type: Journal    
DOI: 10.1111/1467-9868.00170     Document Type: Article

References (23)

1. Agresti, A. (1990) Categorical Data Analysis. New York: Wiley.
2. Baker, S. G. and Laird, N. M. (1988) Regression analysis for categorical variables with outcome subject to nonignorable nonresponse. J. Am. Statist. Ass., 83, 62-69.
3. Chambers, R. L. and Welsh, A. H. (1993) Log-linear models for survey data with non-ignorable non-response. J. R. Statist. Soc. B, 55, 157-170.
4. Cowles, M. K. and Carlin, B. P. (1996) Markov chain Monte Carlo convergence diagnostics: a comparative review. J. Am. Statist. Ass., 91, 883-904.
5. Diggle, P. and Kenward, M. G. (1994) Informative drop-out in longitudinal data analysis (with discussion). Appl. Statist., 43, 49-93.
6. Gilks, W. R. and Wild, P. (1992) Adaptive rejection sampling for Gibbs sampling. Appl. Statist., 41, 337-348.
7. Glynn, R. J., Laird, N. M. and Rubin, D. B. (1993) Multiple imputation in mixture models for nonignorable nonresponse with follow-ups. J. Am. Statist. Ass., 88, 984-993.
8. Greenlees, W. S., Reece, J. S. and Zieschang, K. D. (1982) Imputation of missing values when the probability of response depends on the variable being imputed. J. Am. Statist. Ass., 77, 251-261.
9. Ibrahim, J. G. (1990) Incomplete data in generalized linear models. J. Am. Statist. Ass., 85, 765-769.
10. Ibrahim, J. G. and Lipsitz, S. R. (1996) Parameter estimation from incomplete data in binomial regression when the missing data mechanism is nonignorable. Biometrics, 52, 1071-1078.
11. Kirkwood, J. M., Strawderman, M. H., Ernstoff, M. S., Smith, T. J., Borden, E. C. and Blum, R. H. (1996) Interferon alfa-2b adjuvant therapy of high-risk resected cutaneous melanoma: the Eastern Cooperative Oncology Group trial EST 1684. J. Clin. Oncol., 14, 7-17.
12. Laird, N. M. (1978) Nonparametric maximum likelihood estimation of a mixing distribution. J. Am. Statist. Ass., 73, 805-811.
13. Lipsitz, S. R. and Ibrahim, J. G. (1996) A conditional model for incomplete covariates in parametric regression models. Biometrika, 83, 916-922.
14. Little, R. J. A. (1992) Regression with missing X's: a review. J. Am. Statist. Ass., 87, 1227-1237.
15. Little, R. J. A. and Rubin, D. B. (1987) Statistical Analysis with Missing Data. New York: Wiley.
16. Little, R. J. A. and Schluchter, M. (1985) Maximum likelihood estimation for mixed continuous and categorical data with missing values. Biometrika, 72, 497-512.
17. Louis, T. A. (1982) Finding the observed information matrix when using the EM algorithm. J. R. Statist. Soc. B, 44, 226-233.
18. McCullagh, P. and Nelder, J. (1989) Generalized Linear Models, 2nd edn. New York: Chapman and Hall.
19. Robins, J. M., Rotnitzky, A. and Zhao, L. P. (1994) Estimation of regression coefficients when some regressors are not always observed. J. Am. Statist. Ass., 89, 846-866.
20. _(1995) Analysis of semiparametric regression models for repeated outcomes in the presence of missing data. J. Am. Statist. Ass., 90, 106-121.
21. Rubin, D. B. (1987) Multiple Imputation for Nonresponse in Surveys. New York: Wiley.
22. Rubin, D. B. and Schenker, N. (1986) Multiple imputation for interval estimation from simple random samples with ignorable nonresponse. J. Am. Statist. Ass., 81, 366-374.
23. Wei, G. C. and Tanner, M. A. (1990) A Monte Carlo implementation of the EM algorithm and the poor man's data augmentation algorithms. J. Am. Statist. Ass., 85, 699-704.