A sensitivity approach to modeling longitudinal bivariate ordered data subject to informative dropouts

Health Services and Outcomes Research Methodology

Volumn 6, Issue 1-2, 2006, Pages 37-57

  Todem, D ^a   Kim, K ^b   Lesaffre, E ^c  

^a MICHIGAN STATE UNIVERSITY   (United States)

^b UNIVERSITY OF WISCONSIN SCHOOL OF MEDICINE AND PUBLIC HEALTH   (United States)

^c UNIVERSITY OF LEUVEN   (Belgium)

Author keywords

Bivariate ordinal outcomes; Latent variable; Maximum marginal likelihood; Non ignorable drop out; Non parametric density estimation; Repeated measures; Sensitivity analysis; Shared random effects; Threshold crossing model

Indexed keywords

FLUVOXAMINE; PLACEBO;

ANALYTIC METHOD; ANALYTICAL RESEARCH; ARTICLE; CLINICAL TRIAL; COMPUTER GRAPHICS; DEPRESSION; DOSE RESPONSE; HUMAN; MAJOR CLINICAL STUDY; MATHEMATICAL COMPUTING; OBSESSIVE COMPULSIVE DISORDER; PANIC; POSTMARKETING SURVEILLANCE; PRIORITY JOURNAL; SENSITIVITY ANALYSIS; STATISTICAL MODEL; STRUCTURAL EQUATION MODELING; UNSPECIFIED SIDE EFFECT; VALIDITY;

EID: 37849189916     PISSN: 13873741     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10742-006-0008-x     Document Type: Article

References (33)

1. Albert, P.S., Follmann, D.A.: Modeling repeated count data subject to informative dropout. Biometrics 56, 667-677 (2000)
2. Ashford, J.R., Sowden, R.R.: Multivariate probit analysis. Biometrics 26, 535-546 (1970)
3. Brent, R.P.: Algorithms for minimization without derivatives. Prentice Hall, Englewood Cliffs, NJ (1972)
4. Burton, S.W.: A review of the Fluvoxamine and its uses in depression. Int. Clin. Psychopharmacol. 6 (Supplement 3), 1-17 (1991)
5. Catalano, P.J., Ryan, L.M.: Bivariate latent variable models for clustered discrete and continuous outcomes. J. Am. Stat. Assoc. 87, 651-658 (1992)
6. Cook, R.: Assessment of local influence. J. Roy. Stat. Soc. B 48, 133-169 (1986)
7. Dale, J.: Global cross-ratio models for bivariate, discrete, ordered responses. Biometrics 42, 907-917 (1986)
8. Davidian, M., Giltinan, D.M.: Nonlinear models for repeated measurement data. Chapman & Hall Ltd (1998)
9. Davidon, W.: Variable metric methods for minimization. Technical Report ANL-5990, Argonne National Laboratory, Argonne, IL (1959)
10. Diggle, P., Kenward, M.G.: Informative dropout in longitudinal data analysis (with discussion). Appl. Stat. 43, 49-93 (1994)
11. Efron, B., Tibshirani, R.: An introduction to the bootstrap. Chapman and Hall, London (1993)
12. Ekholm, A., Jokinen, J., McDonald, J.W., Smith, P.: Joint regression and association modelling of longitudinal ordinal data. Biometrics 59, 795-803 (2003)
13. Fletcher, R., Powell, M.J.D.: A rapidly convergent descent method for minimization. Comput. J. CJ-6 (2), 163-168 (1963)
14. Kenward, M.G., Goetghebeur, E.J.T., Molenberghs, G.: Sensitivity for incomplete categorical data. Stat. Model. 1, 31-48 (2001)
15. Lesaffre, E., Molenberghs, G., Dewulf, L.: Effects of dropouts in a longitudinal study: An application of a repeated ordinal model. Stat. Med. 15, 1123-1141 (1996)
16. Lesaffre, E., Todem, D., Verbeke, G.: Flexible modelling of the covariance matrix in a linear random effects model. Biometrical J. 42, 807-822 (2000)
17. Lindsay, B.G.: The geometry of mixture likelihoods: A general theory. The Annals of Statistics 11, 86-94 (1983)
18. Little, R., Rubin, D.: Statistical analysis with missing data. John Wiley and Sons, New York (1987)
19. Little, R.J., Rubin, D.B.: Adjusting for nonignorable drop-out using semiparametric nonresponse models: Comment. J. Am. Stat. Assoc. 94, 1130-1132 (1999)
20. McCullagh, P., Tibshirani, R.: A simple method for the adjustment of profile likelihoods. J. Roy. Stat. Soc. B 52, 325-344 (1990)
21. Molenberghs, G., Kenward, M.G., Lesaffre, E.: The analysis of longitudinal ordinal data with non-random dropout. Biometrika 84, 33-44 (1997)
22. Morrell, C., Pearson, J.D., Brant, L.J.: Linear transformations of linear mixed effects models. Am. Stat. 51, 338-343 (1997)
23. Newton, M., Quintana, F., Zhang, Y.: Nonparametric Bayes methods using predictive updating. Springer, New York, (1998) in practical nonparametric and semiparametric bayesian statistics, dey, d., müller, p., and sinha, d.- edition.
24. Newton, M., Zhang, Y.: A recursive algorithm for nonparametric analysis with missing data. Biometrika 86, 15-26 (1999)
25. Plackett, R.L.: A class of bivariate distribution. J. Am. Stat. Assoc. 60, 516-522 (1965)
26. Rotnitzky, A., Scharfstein, D., Su, T.-L., Robins, J.: Methods for conducting sensitivity analysis of trials with potentially nonignorable competing causes of censoring. Biometrics 57, 103-113 (2001)
27. Scharfstein, D.O., Rotnitzky, A., Robins, J.M.: Adjusting for nonignorable drop-out using semiparametric nonresponse models (c/r: P1121-1146). J. Am. Stat. Assoc. 94, 1096-1120 (1999)
28. Tao, H., Palta, M., Yandell, B., Newton, M.: An estimation method for the semiparametric mixed effects model. Biometrics 55, 102-110 (1999)
29. Ten Have, T., Morabia, A.: Mixed effects models with bivariate and univariate association parameters for longitudinal bivariate binary response data. Biometrics 55, 85-93 (1999)
30. Ten Have, T.R., Reboussin, B.A., Miller, M.E., Kunselman, A.: Mixed effects logistic regression models for multiple longitudinal binary functional limitation responses with informative drop-out and confounding by baseline outcomes. Biometrics 58, 137-144 (2002)
31. Todem, D., Kim, K., Lesaffre, E.: Latent-variable models for longitudinal data with bivariate ordinal outcomes. Technical Report 169, Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison (2002)
32. Verbeke, G., Molenberghs, G., Thijs, H., Lesaffre, E., Kenward, M.: Sensitivity analysis for nonrandom dropout: A local influence approach. Biometrics 57, 7-14 (2001)
33. Wu, M.C., Carroll, R.J.: Estimation and comparison of changes in the presence of informative right censoring by modeling the censoring process (corr: V45 p1347; V47 p357). Biometrics 44, 175-188 (1988)