Biased estimation of adjusted odds ratios from incomplete covariate data due to violation of the missing at random assumption

Biometrical Journal

Volumn 39, Issue 1, 1997, Pages 13-28

  Vach, Werner ^a   Illi, Sabina ^b  

^a UNIVERSITY OF FREIBURG   (Germany)

^b UNIVERSITY OF MUNICH   (Germany)

Author keywords

Adjusted odds ratio; Biased estimation; Case control study; Complete case analysis; Logistic model; Maximum likelihood estimation; Missing at random; Missing value

Indexed keywords

ADJUSTED ODD RATIO; BIASED ESTIMATION; CASE ANALYSIS; CASE-CONTROL STUDY; COMPLETE CASE ANALYSE; LOGISTICS MODEL; MAXIMUM-LIKELIHOOD ESTIMATION; MISSING AT RANDOMS; MISSING VALUES; ODD RATIOS;

MAXIMUM LIKELIHOOD ESTIMATION;

EID: 0031490415     PISSN: 03233847     EISSN: None     Source Type: Journal    
DOI: 10.1002/bimj.4710390103     Document Type: Article

References (23)

1. BAKER, S. G., 1996: Reader reaction: The analysis of categorical case-control data subject to nonignorable nonresponse. Biometrics 52, 362-369.
2. BRESLOW, N. E. and CAIN, K. C., 1988: Logistic regression for two-stage case-control data. Biometrika 75, 11-20.
3. BRESLOW, N. E. and DAY, N. E. 1980: Statistical methods in cancer research, Vol. 1 - The analysis of case-control studies. IARC Scientific Publications No. 32. Lyon.
4. CAIN, K. C. and BRESLOW, N., 1988: Logistic regression analysis and efficient design for two stage studies. American Journal of Epidemiology 128, 1198-1206.
5. CARROLL, R. J. and WAND, M. P., 1991: Semiparametric estimation in logistic measurement error models. Journal of the Royal Statistical Society B 53, 573-585.
6. CARROLL, R. J., WANG, S. and WANG, C. Y., 1995: Prospective analysis of logistic case-control studies. Journal of the American Statistical Association 90, 157-169.
7. COSSLETT, S. R. 1981. Efficient estimation of discrete choice models. In: C. F. Manski, D. McFadden (ed.): Structural analysis of discrete data with econometric applications. MIT Press, Cambridge MA, 51-111.
8. DEMPSTER, A. P., LAIRD, N. M., and RUBIN, D. B., 1977: Maximum likelihood estimation from incomplete data via EM algorithm (with discussion). Journal of the Royal Statistical Society B 39, 1-38.
9. 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.
10. GLYNN, R. J. and LAIRD, N. M., 1983: Regression estimates and missing data: complete case analysis. Technical Report.
11. IBRAHIM, J. G., 1990: Incomplete data in generalized linear models. Journal of the American Statistical Association 85, 765-769.
12. LEHMANN, E. L. 1983: Theory of point estimation. Wiley, New York.
13. PEPE, M. S. and FLEMING, T. R., 1991: A nonparametric method for dealing with mismeasured covariate data. Journal of the American Statistical Association 86, 108-113.
14. REILLY, M. and PEPE, M., 1995: A Mean Score method for missing and auxiliary covariate data in regression models. Biometrika 82, 299-314.
15. 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.
16. RUBIN, D. B., 1976: Inference and missing data. Biometrika 63, 581-592.
17. VACH, W. 1994: Logistic regression with missing values in the covariates. Lecture Notes in Statistics 86. Springer. New York.
18. VACH, W. and BLETTNER, M., 1991: Biased estimation of the odds ratio in case-control studies due to the use of ad-hoc methods of correcting for missing values for confounding variables. American Journal of Epidemiology 134, 895-907.
19. VACH, W. and BLETTNER, M., 1995: Logistic regression with incompletely observed categorical covariates - Investigating the sensitivity against violation of the missing at random assumption. Statistical in Medicine 14, 1315-1329.
20. VACH, W. and SCHUMACHER, M., 1993: Logistic regression with incompletely observed categorical covariates - A comparison of three approaches. Biometrika 80, 353-362.
21. WACHOLDER, S. and WEINBERG, C. R., 1994: Flexible maximum likelihood methods for assessing joint effects in case-control studies with complex sampling. Biometrics 50, 350-357.
22. WEINBERG, C. R. and WACHOLDER, S., 1993: Prospective analysis of case-control data under general multiplicative-intercept risk models. Biometrika 80, 461-465.
23. WHITE, J. E., 1982: A two-stage design for the study of the relationship between a rare exposure and a rare disease. American Journal of Epidemiology 115, 119-128.