Doubly robust and multiple-imputation-based generalized estimating equations

Journal of Biopharmaceutical Statistics

Volumn 21, Issue 2, 2011, Pages 202-225

  Birhanu, Teshome ^a   Molenberghs, Geert ^a,b   Sotto, Cristina ^a,c   Kenward, Michael G ^d  

^a HASSELT UNIVERSITY   (Belgium)

^b KU LEUVEN   (Belgium)

^c UNIVERSITY OF THE PHILIPPINES   (Philippines)

^d LONDON SCHOOL OF HYGIENE AND TROPICAL MEDICINE   (United Kingdom)

Author keywords

Data augmentation; Doubly robust GEE; Missing at random; Multiple imputation; Weighted GEE

Indexed keywords

ARTICLE; HUMAN; MATHEMATICAL VARIABLE; PRIORITY JOURNAL; PROBABILITY; SIMULATION; STATISTICAL ANALYSIS; STUDY DESIGN; TOENAIL ONYCHOMYCOSIS;

ANTIFUNGAL AGENTS; CLINICAL TRIALS AS TOPIC; COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; HUMANS; MODELS, STATISTICAL; ONYCHOMYCOSIS; PATIENT DROPOUTS;

EID: 79952598146     PISSN: 10543406     EISSN: 15205711     Source Type: Journal    
DOI: 10.1080/10543406.2011.550096     Document Type: Article

References (37)

1. Bahadur R. R. (1961). A representation of the joint distribution of responses to n dichotomous items. In: Solomon, H., ed. Studies in Item Analysis and Prediction, Stanford Mathematical Studies in the Social Sciences VI. Stanford, CA: Stanford University Press.
2. Bang, H., Robins, J. M. (2005). Doubly robust estimation in missing data and causal inference models. Biometrics 61:962-972. (Pubitemid 43906903)
3. Beunckens, C., Sotto, C., Molenberghs, G. (2008). A simulation study comparing weighted estimating equations with multiple imputation based estimating equations for longitudinal data. Computational Statistics and Data Analysis 52:1533-1548 (Pubitemid 350017775)
4. Carey, V. C., Zeger, S. L., Diggle, P. J. (1993). Modelling multivariate binary data with alternating logistic regressions. Biometrika 80:517-526.
5. Carpenter, J. R., Kenward, M. G., Vansteelandt, S. (2006). A comparison of multiple imputation and doubly robust estimation for analyses with missing data. Royal Statistics Society Series A 3:571-584. (Pubitemid 43877183)
6. Clayton, D., Spiegelhalter, D., Dunn, G., Pickles, A. (1998). Analysis of longitudinal binary data from multi-phase sampling (with discussion). Royal Statistics Society Series B 60:71-87. (Pubitemid 128656494)
7. Cochran, W. G. (1977). Sampling Techniques. New York: John Wiley & Sons.
8. Dale, J. R. (1986). Global cross-ratio models for bivariate, discrete, ordered responses. Biometrics 42:909-917. (Pubitemid 17134907)
9. Daniel, R. M. (2008). On Aspects of Robustness and Sensitivity in Missing Data Methods. Unpublished PhD thesis. London: London School of Hygiene and Tropical Medicine.
10. De Backer, M., De Keyser, P., De Vroey, C., Lesaffre, E. (1996). A 12-week treatment for dermatophyte toe onychomycosis: terbinafine 250mg/day vs. itraconazole 200mg/day- a double-blind comparative trial. British Journal of Dermatology 134:16-17. (Pubitemid 26188447)
11. Diggle, P. J., Heagerty, P., Liang, K.-Y., Zeger, S. L. (2002). Analysis of Longitudinal Data. New York: Oxford University Press.
12. Fahrmeir, L., Tutz, G. (2001). Multivariate Statistical Modelling Based on Generalized Linear Models. Heidelberg: Springer.
13. Fitzmaurice, G., Davidian, M., Verbeke, G., Molenberghs, G. (2009). Longitudinal Data Analysis. Boca Raton, FL: CRC/Chapman & Hall.
14. Kenward, M. G., Molenberghs, G. (2009). Last observation carried forward: A crystal ball? Journal of Biopharmaceutical Statistics 19:872-888.
15. Lang, J. B., Agresti, A. (1994). Simultaneously modelling joint and marginal distributions of multivariate categorical responses. Journal of American Statistical Association 89:625-632.
16. Liang, K.-Y., Zeger, S. L. (1986). Longitudinal data analysis using generalized linear models. Biometrika 73:13-22.
17. Liang, K.-Y., Zeger, S. L., Qaqish, B. (1992). Multivariate regression analysis for categorical data. Journal of the Royal Statistical Society, Series B 54:3-40.
18. Little, R. J. A., Rubin, D. B. (2002). Statistical Analysis with Missing Data. New York: John Wiley & Sons.
19. Meng, X.-L. (1994). Multiple-imputation inferences with uncongenial sources of input. Statistical Science 9:538-558.
20. Molenberghs, G., Kenward, M. G. (2007). Missing Data in Clinical Studies. Chichester: John Wiley & Sons.
21. Molenberghs, G., Kenward, M. G., Lesaffre, E. (1997). The analysis of longitudinal ordinal data with non-random dropout. Biometrika 84:33-44.
22. Molenberghs, G., Kenward, M. G., Verbeke, G., Teshome, B. (2011). Pseudo-likelihood estimation for incomplete data. Statistica Sinica (in press).
23. Molenberghs, G., Lesaffre, E. (1994). Marginal modelling of correlated ordinal data using a multivariate Plackett distribution. Journal of American Statistical Association 89:633-644.
24. Molenberghs, G., Lesaffre, E. (1999). Marginal modelling of multivariate categorical data. Statistics in Medicine 18:2237-2255. (Pubitemid 29423492)
25. Molenberghs, G., Verbeke, G. (2005). Models for Discrete Longitudinal Data. New York: Springer.
26. Plackett, R. L. (1965). A class of bivariate distributions. Journal of the American Statistical Association 60:516-522.
27. Roberts, D. T. (1992). Prevalence of dermatophyte onychomycosis in the United Kingdom: Results of an omnibus survey. British Journal of Dermatology 126(suppl. 39):23-27.
28. Robins, J. M., Rotnitzky, A., 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.
29. Rotnitzky, A. (2009). Inverse probability weighted methods. In: Fitzmaurice, G., Davidian, M., Verbeke, G., Molenberghs, G., eds. Longitudinal Data Analysis. Boca Raton, FL: CRC/ Chapman & Hall, pp. 453-476.
30. Rubin, D. B. (1976). Inference and missing data. Biometrika 63:581-592.
31. Rubin, D. B. (1978). Multiple imputations in sample surveys-A phenomenological Bayesian approach to nonresponse. In: Imputation and Editing of Faulty or Missing Survey Data. Washington, DC: U.S. Department of Commerce, pp. 1-23.
32. Rubin, D. B. (1987). Multiple Imputation for Nonresponse in Surveys. New York: John Wiley & Sons.
33. Schafer, J. L. (1999). Multiple imputation: A primer. Statistical Methods in Medical Research 8:3-15. (Pubitemid 29222784)
34. Schafer, J. (2003). Multiple imputation in multivariate problems when the imputation and analysis models differ. Statistica Neerlandica 57:19-35. (Pubitemid 41724435)
35. Scharfstein, D. O., Rotnitzky, A., Robins, J. M. (1999). Adjusting for nonignorable dropout using semi-parametric nonresponse models. Journal of the American Statistical Association 94:1096-1146 (with discussion).
36. Van der Laan, M. J., Robins, J. M. (2003). Unified Methods for Censored Longitudinal Data and Causality. New York: Springer.
37. Verbeke, G., Molenberghs, G. (2000). Linear Mixed Models for Longitudinal Data. New York: Springer.