Doubly Robust Estimates for Binary Longitudinal Data Analysis with Missing Response and Missing Covariates

Biometrics

Volumn 67, Issue 3, 2011, Pages 830-842

  Chen, Baojiang ^a   Zhou, Xiao Hua ^b  

^a UNIVERSITY OF NEBRASKA MEDICAL CENTER   (United States)

^b DEPARTMENT OF VETERANS AFFAIRS   (United States)

Author keywords

Doubly robust; Estimating equation; Missing at random; Missing covariate; Missing response

Indexed keywords

IMAGE SEGMENTATION;

CONSISTENT ESTIMATORS; COVARIATES; DOUBLY ROBUST; ESTIMATING EQUATIONS; LONGITUDINAL DATA; MISSING AT RANDOMS; MISSING COVARIATE; MISSING DATA; MISSING RESPONSE; RESPONSE MODEL;

MAXIMUM PRINCIPLE;

DATA SET; LONGITUDINAL GRADIENT; MATHEMATICAL ANALYSIS; NUMERICAL MODEL;

EID: 80052808295     PISSN: 0006341X     EISSN: 15410420     Source Type: Journal    
DOI: 10.1111/j.1541-0420.2010.01541.x     Document Type: Article

References (47)

1. Bahadur, R. R. (1961). A representation of the joint distribution of responses tondichotomous items. In Studies in Item Analysis and Prediction, H. Solomon (ed), 158-168. Stanford Mathematical Studies in the Social Sciences VI. Stanford, California: Stanford University Press.
2. Bang, H. (2005). Medical cost analysis: Application to colorectal cancer data from the SEER medicare database. Contemporary Clinical Trials 26, 586-597.
3. Bang, H. and Robins, J. M. (2005). Doubly robust estimation in missing data and causal inference models. Biometrics 61, 962-972.
4. Carpenter, J., Kenward, M., and Vansteelandt, S. (2006). A comparison of multiple imputation and inverse probability weighting for analyses with missing data. Journal of the Royal Statistical Society, Series A 169, 571-584.
5. Chen, B., Yi, G. Y., and Cook, R. J. (2010). Weighted generalized estimating functions for incomplete longitudinal response and covariate data that are missing at random. Journal of the American Statistical Association 105, 336-353.
6. Chen, Q., Ibrahim, J. G., Chen, M., and Senchaudhuri, P. (2008). Theory and inference for regression models with missing responses and covariates. Journal of Multivariate Analysis 99, 1302-1331.
7. Cook, R. J., Zeng, L., and Yi, G. Y. (2004). Marginal analysis of incomplete longitudinal binary data: A cautionary note on LOCF imputation. Biometrics 60, 820-828.
8. Copas, J. B. and Eguchi, S. (2001). Local sensitivity approximations for selectivity bias. Journal of the Royal Statistical Society, Series B 63, 871-895.
9. Copas, J. B. and Li, H. G. (1997). Inference for non-random samples (with discussion). Journal of the Royal Statistical Society, Series B 59, 55-96.
10. Copas, J. B. and Shi, J. Q. (2000). Meta-analysis, funnel plots and sensitivity analysis. Biometrics 1, 247-262.
11. Davidian, M., Tsiatis, A. A., and Leon, S. (2005). Semiparametric estimation of treatment effect in a pretest-posttest study without missing data. Statistical Science 20, 261-301.
12. Fitzmaurice, G. M., Molenberghs, G., and Lipsitz, S. R. (1995). Regression models for longitudinal binary data responses with informative drop-outs. Journal of the Royal Statistical Society, Series B 57, 691-704.
13. Fitzmaurice, G. M., Lipsitz, S. R., Molenberghs, G., and Ibrahim J. G. (2001). Bias in estimating association parameters for longitudinal binary responses with drop-outs. Biometrics 57, 15-21.
14. Gilks, W. R. and Wild, P. (1992). Adaptive rejection sampling for Gibbs sampling. Applied Statistics 41, 337-348.
15. Horton, H. J. and Laird, N. M. (1998). Maximum likelihood analysis of generalized linear models with missing covariates. Statistical Methods in Medical Research 8, 37-50.
16. Ibrahim, J. G., Lipsitz, S. R., and Horton, N. (2001). Using auxiliary data for parameter estimation with nonignorable missing outcomes. Applied Statistics 50, 361-373.
17. Ibrahim, J. G., Chen, M.-H., Lipsitz, S. R., and Herring, A. H. (2005). Missing-data methods for generalized linear models. Journal of the American Statistical Association 100, 332-346.
18. Jansen, I., Molenberghs, G., Aerts, M., Thijs, H., and Van Steen, K. (2003). A local influence approach applied to binary data from a psychiatric study. Biometrics 59, 409-418.
19. Kang, J. D. Y. and Schafer, J. L. (2007). Demystifying double robustness: A comparison of alternative strategies for estimating a population mean from incomplete data. Statistical Science 22, 523-539.
20. Kenward, M. G. (1998). Selection models for repeated measurements with nonrandom dropout: An illustration of sensitivity. Statistics in Medicine 17, 2723-2732.
21. Liang, K. Y. and Zeger, S. L. (1986). Longitudinal data analysis using gerneralized linear models. Biometrika 73, 13-22.
22. Lipsitz, S. R, Ibrahim, J. G., and Zhao, L. P. (1999). A new weighted estimating equation for missing covariate data with properties similar to maximum likelihood. Journal of the American Statistical Association 94, 1147-1160.
23. Little, R. J. A. and Rubin, D. B. (2002). Statistical Analysis with Missing Data, 2nd edition. New York: John Wiley.
24. Lunceford, J. K. and Davidian, M. (2004). Stratification and weighting via the propensity score in estimation of causal treatment effects: A comparative study. Statistics in Medicine 23, 2937-2960.
25. Molenberghs, G. and Verbeke, G. (2005). Models for Discrete Longitudinal Data. New York: Springer.
26. Molenberghs, G., Kenward, M. G., and Goetghebeur, E. (2001). Sensitivity analysis for incomplete contingency tables. Applied Statistics 50, 15-29.
27. Pepe, M. S. and Anderson, G. L. (1994). A cautionary note on inference for marginal regression models with longitudinal data and general correlated response data. Communications in Statatistcs, Simulation and Computation 23, 939-951.
28. Robins, J. M. and Rotnitzky, A. (2001). Comment on "Inference for semiparametric models: Some questions and answer, "by P. J. Bickel and J. Kwon Statistical Sinica 11, 920-936.
29. 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.
30. Robins, J. M., Greenland, S., and Hu, F. C. (1999). Estimation of the causal effect of a time-varying exposure on the marginal mean of a repeated binary outcome (with discussion). Journal of the American Statistical Association 94, 687-712.
31. Robins, J. M., Rotnitzky, A., and Scharfstein, D. O. (2000). Sensitivity analysis for selection bias and unmeasured confounding in missing data and causal inference models. In Statistical Models in Epidemiology, the Enviroment, and Clinical Trials, E. M. Halloran and D. Berry (eds), 1-94. New York: Springer-Verlag.
32. Rotnitzky, A. and Wypij, D. (1994). A note on the bias of the estimators with missing data. Biometrics 50, 1163-1170.
33. Rotnitzky, A., Robins, J. M., and Scharfstein, D. O. (1998). Semiparametric regression for repeated outcomes with nonignorable nonresponse. Journal of the American Statistical Association 93, 1321-1339.
34. Schafer, J. L. (1997). Analysis of Incomplete Multivariate Data. New York: Chapman and Hall.
35. Scharfstein, D. O. and Irizarry, R. A. (2003). Generalized additive selection models for the analysis of studies with potentially nonignorable missing outcome data. Biometrics 59, 601-613.
36. Scharfstein, D. O., Rotnitzky, A., and Robins, J. M. (1999). Adjusting for nonignorable drop-out using semiparametric nonresponse models (with discussion). Journal of the American Statistical Association 94, 1096-1120.
37. Seaman, S. and Copas, A. (2009). Doubly robust generalized estimating equations for longitudinal data. Statistics in Medicine 28, 937-955.
38. Shardell, M. and Miller, R. (2008). Weighted estimating equations for longitudinal studies with death and non-monotone missing time-dependent covariates and outcomes. Statistics in Medicine 27, 1008-1025.
39. van Buuren, S. (2007). Multiple imputation of discrete and continuous data by fully conditional specification. Statistical Methods in Medical Research 16, 219-242.
40. Van Der Laan, M. J. and Robins, J. M. (2003). Unified Methods for Censored Longitudinal Data and Causality. New York: Springer.
41. Vansteelandt, S., Rotnitzky, A., and Robins, J. M. (2007). Estimation of regression models for the mean of repeated outcomes under nonignorable nonmonotone nonresponse. Biometrika 94, 841-860.
42. Van Steen, K., Molenberghs, G., and Thijs, H. (2001). A local influence approach to sensitivity analysis of incomplete longitudinal ordinal data. Statistical Modelling: An International Journal 1, 125-142.
43. Verbeke, G. and Molenberghs, G. (2000). Linear Mixed Models for Longitudinal Data. New York: Springer.
44. Verbeke, G., Molenberghs, G., Thijs, H., Lesaffre, E., and Kenward, M. G. (2001). Sensitivity analysis for non-random dropout: A local influence approach. Biometrics 57, 43-50.
45. White, H. (1982). Maximum likelihood estimation under misspecified models. Econometrica 50, 1-26.
46. Zhao, L. P., Lipsitz, S. R., and Lew, D. (1996). Regression analysis with missing covariate data using estimating equations. Biometrics 52, 1165-1182.
47. Zhu, H. T. and Lee, S. Y. (2001). Local inference for incomplete data models. Journal of the Royal Statistical Society, Series B 63, 111-126.