Simple approaches to assess the possible impact of missing outcome information on estimates of risk ratios, odds ratios, and risk differences

Controlled Clinical Trials

Volumn 24, Issue 4, 2003, Pages 411-421

  Magder, Laurence S ^a  

^a UNIVERSITY OF MARYLAND   (United States)

Author keywords

Binary data; Dropout; Missing data; Nonignorable missingness; Nonresponse bias; Odds ratios; Risk differences; Risk ratios; Sensitivity analysis

Indexed keywords

ARTICLE; BINOMIAL DISTRIBUTION; DATA ANALYSIS; MATHEMATICAL COMPUTING; MEDICAL RESEARCH; PROBABILITY; RISK ASSESSMENT; SENSITIVITY ANALYSIS; STATISTICAL ANALYSIS;

EID: 0037629206     PISSN: 01972456     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0197-2456(03)00021-7     Document Type: Article

References (21)

1. Little R.J.A., Rubin D.B. Statistical analysis with missing data. 1987;John Wiley & Sons, New York.
2. Carpenter J., Pocock S., Lamm C.J. Coping with missing data in clinical trials: a model-based approach applied to asthma trials. Stat Med. 21:2002;1043-1066.
3. The Ischemic Optic Neuropathy Decompression Trial Research Group. Ischemic Optic Neuropathy Decompression Trial: twenty-four-month update. Arch Ophthalmol. 118:2000;793-798.
4. Royall R. Statistical evidence: a likelihood paradigm. 1997;Chapman & Hall, New York.
5. Horowitz J.L., Manski C.F. Nonparametric analysis of randomized experiments with missing covariate and outcome data. J Am Stat Assoc. 95:2000;77-84.
6. Blumenthal S. Multinomial sampling with partially categorized data. J Am Stat Assoc. 63:1968;542-551.
7. Elashoff J.D., Elashoff R.M. Two-sample problems for a dichotomous variable with missing data. Applied Statistics. 23:1974;26-34.
8. Hocking R.R., Oxspring H.H. The analysis of partially categorized contingency data. Biometrics. 30:1974;469-483.
9. Chen T., Fienburg S.E. Two-dimensional contingency tables with both completely and partially cross-classified data. Biometrics. 30:1974;629-642.
10. Fuchs C. Maximum likelihood estimation and model selection in contingency tables with missing data. J Am Stat Assoc. 77:1982;270-278.
11. Baker S.G., Laird N.M. Regression analysis for categorical variables with outcome subject to nonignorable nonresponse. J Am Stat Assoc. 83:1988;62-69.
12. Baker S.G., Rosenburg W.F., Dersimonian R. Closed-form estimates for missing counts in two-way contingency tables. Stat Med. 11:1992;643-657.
13. Molenberghs G., Goetghebeur E.J.T., Lipsitz S.R., Kenward M.G. Nonrandom missingness in categorical data: strengths and limitations. American Statistician. 53:1999;110-118.
14. Nordheim E.V. Inference from nonrandomly missing categorical data: an example from a genetics study on Turner's syndrome. J Am Stat Assoc. 79:1984;772-780.
15. Vach W., Blettner M. Logistic regression with incompletely observed categorical covariates - investigating the sensitivity against violation of the missing at random assumption. Stat Med. 14:1995;1315-1329.
16. Scharfstein D.O., Rotnitzky A., Robins J.M. Adjusting for nonignorable drop-out using semiparametric nonresponse models. J Am Stat Assoc. 94:1999;1096-1120.
17. Hollis S. A graphical sensitivity analysis for clinical trials with non-ignorable missing binary outcome. Stat Med. 21:2002;3823-3834.
18. Delucchi K.L. Methods for the analysis of binary outcomes results in the presence of missing data. J Consult Clin Psychol. 62:1994;569-575.
19. Matts J.P., Launer C.A., Nelson E.T., et al. A graphical assessment of the potential impact of losses to follow-up on the validity of study results. Stat Med. 16:1997;1943-1954.
20. Bishop Y.M.M., Feinberg S.E., Holland P.W. Discrete multivariate analysis. 1975;MIT Press, Cambridge, Massachusetts.
21. Kleinbaum D.G., Kupper L.L., Morgenstern H. Epidemiologic research: principles and quantitative methods. 1982;Van Nostrand Rheinhold, New York.