A probit latent class model with general correlation structures for evaluating accuracy of diagnostic tests

Biometrics

Volumn 65, Issue 4, 2009, Pages 1145-1155

  Xu, Huiping ^a   Craig, Bruce A ^a  

^a PURDUE UNIVERSITY   (United States)

Author keywords

Diagnostic tests; Latent class models; Monte Carlo EM algorithm; Parameter expanded EM algorithm; Probit models; Sensitivity; Specificity

Indexed keywords

DIAGNOSIS; MAXIMUM LIKELIHOOD ESTIMATION; PARAMETER ESTIMATION;

CORRELATION STRUCTURE; DIAGNOSTIC TESTS; EM ALGORITHMS; GENERAL CORRELATIONS; LATENT CLASS MODEL; MONTE CARLO EM ALGORITHM; PARAMETER-EXPANDED EM ALGORITHM; PROBIT MODELS; SENSITIVITY; SPECIFICITY;

MONTE CARLO METHODS;

CORRELATION; DISEASE; MONTE CARLO ANALYSIS; SENSITIVITY ANALYSIS;

ALGORITHM; ARTICLE; CORRELATION ANALYSIS; DIAGNOSTIC ACCURACY; DIAGNOSTIC TEST; EVALUATION; LATENT STRUCTURE ANALYSIS; MAXIMUM LIKELIHOOD METHOD; MONTE CARLO METHOD; PARAMETER; SENSITIVITY AND SPECIFICITY; SIMULATION; THEORETICAL MODEL;

EID: 70450227267     PISSN: 0006341X     EISSN: 15410420     Source Type: Journal    
DOI: 10.1111/j.1541-0420.2008.01194.x     Document Type: Article

References (27)

1. Albert, P. S., McShane, L. M., Shih, J. H., and the U.S. National Cancer Institute Bladder Tumor Marker Network 2001). Latent class modeling approaches for assessing diagnostic error without a gold standard: With applications to p53 immunohistochemical assays in bladder tumors. Biometrics 57, 610 619.
2. Ashford, J. R. and Sowden, R. R. (1970). Multi-variate probit analysis. Biometrics 26, 535 546.
3. Basford, K., Greenway, D., Maclachlan, G., and Peel, D. (1997). Standard errors of fitted means under normal mixture model. Computational Statistics 12, 1 17. (Pubitemid 127385683)
4. Bock, D. R. and Gibbons, R. D. (1996). High-dimensional multivariate probit analysis. Biometrics 52, 1183 1194.
5. Chib, S. and Greenburg, E. (1998). Analysis of multivariate probit models. Biometrika 85, 347 361.
6. Collins, L. M., Fidler, P. L., Wugalter, S. E., and Long, J. D. (1993). Goodness-of-fit testing for latent class models. Multivariate Behavioral Research 28, 375 389.
7. De Menezes, L. M. (1999). On fitting latent class models for binary data: The estimation of standard errors. British Journal of Mathematical and Statistical Psychology 52, 149 168.
8. Dempster, A. P., Laird, N. M., and Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B 39, 1 38.
9. Espeland, M. A. and Handelman, S. L. (1989). Using latent class models to characterize and assess relative errors in discrete measurement. Biometrics 45, 587 599.
10. Fay, J. W. J. (1957). The national coal boards pneumoconiasis research. Nature 180, 309.
11. Goodman, L. A. (1974). Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika 61, 215 231.
12. Hadgu, A. and Qu, Y. (1998). A biomedical application of latent class models with random effects. Applied Statistics 47, 603 616.
13. Hagenaars, J. A. (1988). Latent structure models with direct effects between indicators: Local independence models. Sociological Methods and Research 16, 379 405.
14. Horrace, W. C. (2005). Some results on the multivariate truncated normal distribution. Journal of Multivariate Analysis 94, 209 221.
15. Hui, S. L. and Walter, S. D. (1980). Estimating the error rates of diagnostic tests. Biometrics 36, 167 171.
16. Kinderman, A. J. and Monahan, J. F. (1977). Computer generation of random variables using the ratio of uniform deviates. ACM Transactions on Mathematical Software 3, 257 260.
17. Kou, S. G., Liu, C., and Wu, Y. N. (1998). Cumulative implementation of Monte Carlo EM. Technical report, Department of Statistics, University of Michigan.
18. Langeheine, R., Pannekoek, J., and Van De Pol, F. (1996). Bootstrapping goodness-of-fit measures in categorical data analysis. Sociological Methods and Research 24, 492 516.
19. Liu, C., Rubin, D. B., and Wu, Y. N. (1998). Parameter expansion to accelerate EM: The PX-EM algorithm. Biometrika 85, 755 770.
20. 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.
21. Robert, C. P. (1995). Simulation of truncated normal variables. Statistics and Computing 5, 121 125.
22. Rogan, J. M., Ashford, J. R., Chapman, P. J., Duffield, D. P., Fay, J. W. J., and Rae, S. (1961). Pneumoconiosis and respiratory symptoms at eight collieries. British Medical Journal 1, 1337 1352.
23. Uebersax, J. S. (1999). Probit latent class analysis with dichotomous or ordered category measures: Conditional independence/dependence models. Applied Psychological Measurement 23, 283 297.
24. Uebersax, J. S. and Grove, W. M. (1990). Latent class analysis of diagnostic agreement. Statistics in Medicine 9, 559 572.
25. Vacek, P. M. (1985). The effect of conditional dependence on the evaluation of diagnostic tests. Biometrics 41, 959 968.
26. Wei, G. C. and Tanner, M. A. (1990). A Monte Carlo implementation of the EM algorithm and the poor man's data augmentation algorithms. Journal of the American Statistical Association 85, 699 704.
27. Yang, I. and Becker, M. P. (1997). Latent variable modeling of diagnostic accuracy. Biometrics 53, 948 958.