Robust inference for longitudinal data analysis with non-ignorable and non-monotonic missing values

Statistics and its Interface

Volumn 5, Issue 4, 2012, Pages 479-490

  Tseng, Chi hong ^a   Elashoff, Robert ^a   Li, Ning ^b   Li, Gang ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b CEDARS SINAI MEDICAL CENTER   (United States)

Author keywords

Huber function; Missing not at random; Pseudo likelihood; t distribution

Indexed keywords

EID: 84876335926     PISSN: 19387989     EISSN: 19387997     Source Type: Journal    
DOI: 10.4310/sii.2012.v5.n4.a11     Document Type: Article

References (36)

1. Abramowitz, M. and Stegun, I. A. (1972). Handbook of Mathematical Functions, 10th ed.
2. Brown, E. R. and Ibrahim, J. G. (2003). A Bayesian semiparametric joint hierarchical model for longitudinal and survival data. Biometrics 59, 221-228. MR1987388
3. Cantoni, E. and Ronchetti, E. (2001). Robust Inference for Generalized Linear Models. Journal of the American Statistical Association 96, 1022-1030. MR1947250
4. Elashoff, R. M., Li, G., and Li, N. (2007). An approach to joint analysis of longitudinal measurements and competing risks failure time data. Statistics in Medicine 26, 2813-2835. MR2370939
5. Elashoff, R. M., Li, G., and Li, N. (2008). A joint model for longitudinal measurements and survival data in the presence of multiple failure types. Biometrics 64, 762-771. MR2526626
6. Ghidey, W., Lesaffre, E., and Eilers, P. (2004). Smooth random effects distribution in a linear mixed model. Biometrics 60, 945-953. MR2133547
7. Gill, P. (2000). A robust mixed linear model analysis for longitudinal data. Statistics in Medicine 19, 975-987.
8. Gong, G. and Samaniego, F. (1981). Pseudo maximum-likelihood estimation-theory and applications. Annals of Statistics 9, 861-869. MR0619289
9. Hansen, L. (1982). Sample properties of generalized method of moments estimators. Econometrica 50, 1029-1054. MR0666123
10. Hogan, J. W., Roy, J. and Korkontzelou, C. (2004). Biostatistics tutorial: Handling dropout in longitudinal data. Statistics in Medicine 23, 1455-1497.
11. Huggins, R. (1993). A robust approach to the analysis of repeated measures. Biometrics 49, 715-720. MR1243487
12. Ibrahim, J. and Molenberghs, G. (2009). Missing data methods in longitudinal studies: A review. TEST 18, 1-43. MR2495958
13. Ibrahim, J. G., Chen, M.-H., and Lipsitz, S. R. (2001). Missing responses in generalized linear mixed models when the missing data mechanism is nonignorable. Biometrika 88, 551-564. MR1844851
14. Laird, N. M. and Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics 38, 963-974.
15. Lange, K. L., Little, R. J. A., and Taylor, J. M. G. (1989). Robust statistical modeling using the t-distribution. Journal of the American Statistical Association 84, 881-896. MR1134486
16. Li, N., Elashoff, R. M., and Li, G. (2009). Robust joint modeling of longitudinal measurements and competing risks failure time data. Biometrical Journal 51, 19-30. MR2667508
17. Liang, K. and Zeger, S. (1986). Longitudinal data analysis using generalized linear model. Biometrika 73, 13-22. MR0836430
18. Little, R. J. A. (1993). Pattern-mixture models for multivariate incomplete data. Journal of the American Statistical Association 88, 125-134.
19. Little, R. J. A. and Rubin, D. B. (2002). Statistical Analysis with Missing Data, 2nd ed. MR1925014
20. Ma, G., Troxel, A., and Heitjan, D. (2005). An index of local sensitivity to nonignorable drop-out in longitudinal modeling. Statistics in Medicine 24, 2129-2150. MR2146923
21. Parzen, M., Lipsitz, S., Fitzmaurice, G., Ibrahim, J., and Troxel, A. (2006). Pseudo-likelihood methods for longitudinal binary data with non-ignorable missing responses and covariates. Statistics in Medicine 25, 2784-2796. MR2242203
22. Pinheiro, J., Liu, C., and Wu, Y. N. (2001). Efficient algorithms for robust estimation in linear mixed-effects models using the multivariate t-distribution. Journal of Computational and Graphical Statistics 10, 249-276. MR1939700
23. Richardson, A. and Welsh, A. (1995). Robust restricted maximum likelihood in mixed linear models. Biometrics 51, 1429-1439.
24. Rizopoulos, D., Verbeke, G., and Molenberghs, G. (2008). Shared parameter models under random effects misspecification. Biometrika 95, 63-71. MR2409715
25. Schluchter, M. (1992). Methods for the analysis of informatively censored longitudinal data. Statistics in Medicine 11, 1861-1870.
26. Tashkin, D. P., Elashoff, R., Clements, P. J., Goldin, J., Roth, M. D., Furst, D. E., Arriola, E., Silver, R., Strange, C., Bolster, M., Seibold, J. R., Riley, D. J., Hsu, V. M., Varga, J., Schraufnagel, D. E., Theodore, A., Simms, R., Wise, R., Wigley, F., White, B., Steen, V., Read, C., Mayes, M., Parsley, E., Mubarak, K., Connolly, M. K., Golden, J., Olman, M., Fessler, B., Rotheld, N., Metersky, M., and the Scleroderma Lung Study Research Group (2006). Cyclophosphamide versus placebo in scleroderma lung disease. N. Engl. J. Med. 354, 2655-2666.
27. Troxel, A. B., Lipsitz, S., and Harrington, D. (1998). Marginal models for the analysis of longitudinal measurements with nonignorable non-monotone missing data. Biometrika 85, 661-672. MR1665826
28. Tukey, J. (1949). One degree of freedom for non-additivity. Biometrics 5, 232-242.
29. Verbeke, G. and Lesaffre, E. (1996). A linear mixed model with heterogeneity in the random-effects population. Journal of the American Statistical Association 91, 217-221.
30. Verbeke, G., Molenberghs, G., Thijs, H., Lesare, E., and Kenward, M. (2001). Sensitivity analysis for nonrandom dropout: A local influence approach. Biometrics 57, 7-14. MR1833286
31. Verbeke, G., Molenberghs, G., and Beunckens, C. (2008). Formal and informal model selection with incomplete data. Statistical Science 23, 201-218. MR2516820
32. Wang, C. Y. and Pepe, M. S. (2000). Expected estimating equations to accommodate covariate measurement error. Journal of the Royal Statistical Society, Series B 62, 509-524. MR1772412
33. Wang, C. Y., Huang, Y., Chao, E. C. and Jeffcoat, M. K. (2008). Expected estimating equations for missing data, measurement error, and misclassification, with application to longitudinal nonignorable missing data. Biometrics 64, 85-95. MR2422822
34. White, H. (1982). Maximum likelihood estimation of misspecified models. Econometrica 50, 1-25. MR0640163
35. Wu, M. and Carroll, R. (1988). Estimation and comparison of change in the presence of informative right censoring by modeling the censoring process. Biometrics 44, 175-188. MR0931633
36. Zhang, D. and Davidian, M. (2001). Linear mixed models with flexible distributions of random effects for longitudinal data. Biometrics 57, 795-802. MR1859815