Robustness of the latent variable model for correlated binary data

Biometrics

Volumn 55, Issue 1, 1999, Pages 258-263

  Tan, Ming ^a   Qu, Yinsheng ^b   Sunil Rao, J ^b  

^a ST JUDE CHILDREN S RESEARCH HOSPITAL   (United States)

^b LERNER RESEARCH INSTITUTE   (United States)

Author keywords

GEE; Latent variable model; Sensitivity; Simulation

Indexed keywords

ITERATIVE METHODS; PARAMETER ESTIMATION; TECHNOLOGY TRANSFER; VECTORS;

BINARY DATA; GEE; LATENT VARIABLE MODELING; LATENT VECTORS; MULTIVARIATE NORMAL; PARAMETERS ESTIMATES; REGRESSION MODELLING; SENSITIVITY; SHOW THROUGH; SIMULATION;

REGRESSION ANALYSIS;

ARTICLE; BIOMETRY; COVARIANCE; DATA ANALYSIS; REGRESSION ANALYSIS; STATISTICAL ANALYSIS; STATISTICAL MODEL;

EID: 0032929197     PISSN: 0006341X     EISSN: None     Source Type: Journal    
DOI: 10.1111/j.0006-341X.1999.00258.x     Document Type: Article

References (18)

1. Bartholomew, D. J. (1987). Latent Variable Models and Factor Analysis. London: Charles Griffin & Co., Ltd.
2. Catalano, P. J. and Ryan, L. M. (1992). Bivariate latent variable models for clustered discrete and continuous outcomes. Journal of the American Statistical Association 87, 651-658.
3. Dunn, G. (1989). Design and Analysis of Reliability Studies: The Statistical Evaluation of Measurement Errors. New York: Oxford University Press.
4. Finney, D. J. (1971). Probit Analysis, 3rd edition. Cambridge: Cambridge University Press.
5. Hanley, J. A. (1989). Receiver operator characteristics (ROC) methodology: The state of the art. CRC Critical Reviews in Diagnostic Imaging 29, 307-335.
6. Hart, J. D. (1995). Some automated methods of smoothing time-dependent data. Technical Report, Department of Statistics, Texas A&M University, College Station.
7. Liang, K. Y. and Zeger, S. (1986). Longitudinal data analysis using generalized linear models. Biometrika 73, 13-22.
8. Liang, K. Y., Zeger, S. L., and Qaqish, B. (1992). Multivariate regression analysis for categorical data (with discussion). Journal of the Royal Statistical Society, Series B 54, 3-40.
9. Morgan, B. J. T. (1992). Analysis of Quantal Response Data. London: Chapman and Hall.
10. Prentice, R. L. (1988). Correlated binary regression with co-variates specific to each binary observation. Biometrics 44, 1033-1048.
11. Qaqish, B. F. and Liang, K. Y. (1992). Marginal models for correlated binary responses with multiple classes and multiple levels of nesting. Biometrics 48, 939-950.
12. Qu, Y., Williams, G. W., Beck, G. J., and Medendorp, S. V. (1992). Latent variables models for clustered dichotomous data with multiple subclusters. Biometrics 48, 1095-1102.
13. Qu, Y., Piedmonte, M. R., and Medendorp, S. V. (1995). Latent variable model for clustered ordinal data. Biometrics 51, 268-275.
14. Qu, Y., Tan, M., and Kutner, M. H. (1996). Random-effects models in latent class analysis for evaluating accuracy of diagnostic tests. Biometrics 52, 797-810.
15. Sammel, M. D. and Ryan, L. M. (1996). Latent variable models with fixed effects. Biometrics 52, 650-663.
16. Tan, M., Qu, Y., and Kutner, M. H. (1997). Model diagnostics for marginal regression analysis of correlated binary data. Communications in Statistics, Series B 26, 539-558.
17. Zeger, S. L. and Liang, K. Y. (1986). Longitudinal data analysis for discrete and continuous outcomes. Biometrics 42, 121-130.
18. Zhao, L. P. and Prentice, R. L. (1990). Correlated binary regression using a quadratic exponential model. Biometrika 77, 642-648.