GEE with Gaussian estimation of the correlations when data are incomplete

Biometrics

Volumn 56, Issue 2, 2000, Pages 528-536

  Lipsitz, Stuart R ^a,b   Molenberghs, Geert ^c   Fitzmaurice, Garrett M ^d   Ibrahim, Joseph ^a  

^a DANA FARBER CANCER INSTITUTE   (United States)

^b MEDICAL UNIVERSITY OF SOUTH CAROLINA   (United States)

^c HASSELT UNIVERSITY   (Belgium)

^d HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

Author keywords

Binary response; Missing data; Multiple imputation; Weighted estimating equations

Indexed keywords

BINARY RESPONSE; CORRELATION PARAMETERS; ESTIMATING EQUATIONS; GAUSSIANS; GENERALIZED ESTIMATING EQUATIONS; MISSING AT RANDOMS; MISSING DATA; MULTIPLE IMPUTATION; SIMULATION STUDIES; WEIGHTED ESTIMATING EQUATION;

PARAMETER ESTIMATION;

DIDANOSINE; ZIDOVUDINE;

ARTICLE; CALCULATION; CLINICAL TRIAL; HUMAN; HUMAN IMMUNODEFICIENCY VIRUS INFECTION; SIMULATION;

EID: 0033919665     PISSN: 0006341X     EISSN: None     Source Type: Journal    
DOI: 10.1111/j.0006-341X.2000.00528.x     Document Type: Article

References (15)

1. Bahadur, R. R. (1961). A representation of the joint distribution of responses to n dichotomous items. In Studies in Item Analysis and Prediction, H. Solomon (ed), 158-168, Stanford Mathematical Studies in the Social Sciences, Volume VI. Stanford, California: Stanford University Press.
2. Crowder, M. J. (1985). Gaussian estimation for correlated binomial data. Journal of the Royal Statistical Society, Series B 47, 229-237.
3. Finkelstein, D. M., Willianis, P. L., Molenberghs, G., Feinberg, J., Powderly, W., Kahn, J., Dolins, R., and Cotton, D. (1996). Patterns of opportunistic infections in patients with HIV infection. Journal of Acquired Immune Deficiency Syndrome and Human Retroviruses 12, 38-45.
4. Huber, P. J. (1967). The behavior of maximum likelihood estimates under nonstandard conditions. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Volume 1, 221-233. Berkeley: University of California Press.
5. Laird, N. M. (1988). Missing data in longitudinal studies. Statistics in Medicine 7, 305-315.
6. Liang, K. Y. and Zeger, S. L. (1986). Longitudinal data nalysis using generalized linear models. Biometrika 73, 13-22.
7. 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.
8. Paik, M. (1997). The generalized estimating equation approach when data are not missing completely at random. Journal of the American Statistical Association 92, 1320-1329.
9. Prentice, R. L. (1988). Correlated binary regression with covariates specific to each binary observation. Biometrics 44, 1033-1048.
10. Robins, J. M., Rotnitzky, A., and Zhao, L. P. (1995). Analysis of semiparametric regression models for repeated outcomes in the presence of missing data. Journal of the American Statistical Association 90, 106-121.
11. Royall, R. M. (1986). Model robust confidence intervals using maximum likelihood estimators. International Statistical Review 54, 221-226.
12. Rubin, D. B. (1976). Inference and missing data. Biometrika 63, 581-592.
13. SAS. (1996). SAS/STAT Software: Changes and Enhancements through Release 6.12. Cary, North Carolina: SAS Institute.
14. White, H. (1982). Maximum likelihood estimation under misspecified models. Econometrica 50, 1-26.
15. Whittle, P. (1961). Gaussian estimation in stationary time series. Bulletin of the International Statistical Institute 39, 1-26.