Regression analysis with missing covariate data using estimating equations

Biometrics

Volumn 52, Issue 4, 1996, Pages 1165-1182

  Zhao, Lue Ping ^a   Lipsitz, Stuart ^b   Lew, Danika ^a  

^a Fred Hutchinson Cancer Research Center   (United States)

^b HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

Author keywords

Estimating equations; Generalized linear model; Joint estimating equations; Missing at random; Missing completely at random; Missing covariate data; Weighted estimating equations

Indexed keywords

NORMAL DISTRIBUTION; SAMPLING;

COVARIATES; ESTIMATING EQUATIONS; GENERALIZED LINEAR MODEL; JOINT ESTIMATING EQUATION; MISSING AT RANDOMS; MISSING COMPLETELY AT RANDOM; MISSING COVARIATE DATA; WEIGHTED ESTIMATING EQUATION;

REGRESSION ANALYSIS;

ARTICLE; BIOMETRY; DATA ANALYSIS; DIET; REGRESSION ANALYSIS; STATISTICAL ANALYSIS; THYROID CANCER;

TURDUS;

EID: 12644278870     PISSN: 0006341X     EISSN: None     Source Type: Journal    
DOI: 10.2307/2532833     Document Type: Article

References (16)

1. Breslow, N. E. and Cain, K. C. (1988). Logistic regression for two-stage case-control data. Biometrika 75, 11-20.
2. Carroll, R. J. and Wand, M. P. (1991). Semiparametric estimation in logistic measurement error models. Journal of the Royal Statistical Society, Series B 53, 573-585.
3. Flanders, W. D. and Greenland, S. (1991). Analytic methods for two-stage case-control studies and other stratified designs. Statistics in Medicine 10, 739-747.
4. GAUSS. (1991). The GAUSS System, Version 2.2. Kent, Washington: Aptech Systems, Inc.
5. Kolonel, L. N., Hankin, J. H., Wilkens, L. R., Fukunaga, F. H., and Hinds, M. W. (1990). An epidemiologic study of thyroid cancer in Hawaii. Cancer Causes and Control 1, 223-234.
6. Liang, K. Y. and Zeger, S. L. (1986). Longitudinal data analysis using generalized linear model. Biometrika 73, 13-22.
7. Little, R. J. A. (1992). Regression with missing X's: A review. Journal of the American Statistical Association 87, 1227-1237.
8. McCullagh, R. and Nelder, J. A. (1989). Generalized Linear Models, 2nd edition. London: Chapman and Hall.
9. Pepe, M. S. and Fleming, T. R. (1991). A general nonparametric method for dealing with errors in missing or surrogate data. Journal of the American Statistical Association 86, 108-113.
10. Prentice, R. L. (1988). Correlated binary regression with covariates specific to each binary observation. Biometrics 44, 1033-1048.
11. Prentice, R. L. and Zhao, L. P. (1991). Estimating equations for parameters in means and covariances of multivariate discrete and continuous responses. Biometrics 47, 825-839.
12. Reilly, M. and Pepe, M. S. (1995). A mean-score method for missing and auxiliary covariate data in regression models. Biometrika 82, 299-314.
13. Robins, J. M., Rotnitzky, A., and Zhao, L. P. (1994). Estimation of regression coefficients when some regressors are not always observed. Journal of the American Statistical Association 89, 846-866.
14. Rubin, D. B. (1976). Inference and missing data. Biometrika 63, 581-592.
15. Zhao, L. P. and Lipsitz, S. (1992). Design and analysis of two-stage studies. Statistics in Medicine 11, 769-782.
16. Zhao, L. P. and Prentice, R. L. (1991). Use of a quadratic exponential model to generate estimating equations for means, variances, and covariances. In Estimating Functions, V. P. Godambe (ed), 103-117. Oxford: Oxford Science Publications.