Latent Variable Models for Teratogenesis Using Multiple Binary Outcomes

Journal of the American Statistical Association

Volumn 92, Issue 437, 1997, Pages 13-20

  Legler, Julie M ^a   Ryan, Louise M ^b  

^a NATIONAL INSTITUTES OF HEALTH   (United States)

^b HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

Author keywords

Item response theory; Random effects; Teratology

Indexed keywords

EID: 0031507509     PISSN: 01621459     EISSN: 1537274X     Source Type: Journal    
DOI: 10.1080/01621459.1997.10473598     Document Type: Article

References (34)

1. Agresti, A. (1993). “Distribution-Free Fitting of Logit Models With Random Effects for Repeated Categorical Responses,” Statistics in Medicine, 12, 1969-1988.
2. Baker, F. B. (1992), Item Response Theory: Parameter Estimation Techniques, New York: Marcel Dekker.
3. Bartholomew, D. J. (1987), Latent Variable Models and Factor Analysis, Oxford, U.K.: Oxford University Press.
4. Bimbaum, A. (1968). “Some Latent Trait Models and Their Use in Inferring an Examinee’s Ability,” in Statistical Theories of Mental Test Scores, eds. F. M. Lord and M. R. Novick, Reading, MA: Addison-Wesley, pp. 397-372.
5. Bock, R. D., and Aitkin, M. (1970), “Marginal Maximum Likelihood Estimation of Item Parameters. An Application of an EM Algorithm,” Psychometrika, 37,29-51.
6. Conaway, M. R. (1989), “Analysis of Repeated Categorical Measurements With Conditional Likelihood Methods:’ Journal of the American Statistical Association, 84, 53-62.
7. Conaway, M. R. (1992) “The Analysis of Repeated Categorical Measurements Subject to Nonignorable Nonresponse,” Journal of the American Statistical Association, 87, 81 7-824.
8. Cressie, N., and Holland, P. (1983). “Characterizing the Manifest Probabilities of Latent Trait Models:’ Psychometrika, 48, 129-141.
9. Dempster, A. P., Laird, N. M., and Rubin, D. B. (1977). “Maximum Likelihood from incomplete data via the EM Algorithm” (with discussion), Journal of the Royal Statistical Society, Ser. B, 39, 1-38.
10. Everitt, B. S. (1984), “An Introduction to Latent Variable Models,” London: Chapman and Hall.
11. Fischer, G. H., and Molenaar, I. W. (1995). Rasch Models: Foundations, Recent Developments, and Applications, New York: Springer-Verlag.
12. Hambleton, R. K., Swaminathan, H., and Rogers, H. J. (1991). Fundamentals of Item Response Theory, London: Sage Publications.
13. Holmes, L. B. (1988), “Human Teratogens: Delineating the Phenotypic Effects, the Period of Greatest Sensitivity, and the Dose-Response Relationship and Mechanisms of Action,” in Transplacental Eflects on Fetal Health, New York: Alan R. Liss, pp. 177-191.
14. Holmes, L. B., Harvey, E. A., Brown, K. S., and Khoshbin, S. (1993), “Anticonvulsant Teratogenesis: 1. A Study Design for Newborn Infants,” Teratology, 49, 202-207.
15. Lawley, D. N. (1943). “On Problems Connected With Item Selection and Test Construction,” Proceedings of the Royal Society of Edinburgh, 61A
16. Lefkopoulou, M., Moore, D., and Ryan, L. (1989), “The Analysis of Multiple Correlated Binary Outcomes: Application to Rodent Teratology Experiments:’ Journal of the American Statistical Association, 84, 810-815.
17. Lefkopoulou, M., and Ryan, L. (1993). “Global Tests for Multiple Binary Outcomes,” Biometrics, 49, 975-996.
18. Legler, J., Lefkopoulou, M., and Ryan, L. (1995). “Efficiency and Power of Tests for Multiple Binary Outcomes,” Journal of the American Statistical Association, 90, 680-693.
19. Legler, J., Ryan, L., Harvey, E., and Holmes, L. (1994). “Anticonvulsant Teratogenesis: 2. Statistical Methods for Multiple Birth Outcomes:’ Teratology, 50, 74-79.
20. Liang, K.-Y., and Zeger, S. L. (1986). “Longitudinal Data Analysis Using Generalized Linear Models,” Biometrika, 73, 13-22.
21. Liu, X., and Liang, K.-Y. (1991), “Adjustment for Non-Differential Misclassification Error in the Generalized Linear Model,” Statistics in Medicine, 10, 1197-1211.
22. Lord, F. M. (1953), “An Application of Confidence Intervals and of Maximum Likelihood to the Estimation of an Examinee’s Ability,” Psychometrika, 18, 57-75.
23. Louis, T. A. (1982). “Finding the Observed Information Matrix When Using the EM Algorithm,” Journal of the Royal Statistical Society, Ser. B, 44,226-233.
24. Mislevy, R. I. (19854, “Estimating Latent Group Effects:’ Journal of the American Statistical Association, 80, 993-997.
25. Mislevy, R. J., and Bock, R. D. (1990). BILOG 3: Item Analysis and Test Scoring With Binary Logistic Models, Mooresville, IN: Scientific Software, Inc.
26. Neyman, J., and Scott, E. L. (1948), “Consistent Estimates Based on Partially Consistent Observations,” Econometrika, 16, 1-32.
27. O’Brien, P. (1984), “Procedures for Comparing Samples With Multiple Endpoints,” Biometrics, 40, 1079-1087.
28. Rasch, G. (1966). “An Item Analysis Which Takes Individual Differences Into Account,” British Journal of Mathematical and Statistical Psychology, 19,48-51. NC
29. SAS Institute Inc. (1989). SAS/STA7@ Users Guide, (Vol. 1; 4th ed.) Cary
30. Skene, A. M., and White, S. A. (1992), “A Latent Class Model for Repeated-Measurements Experiments,” Statistics in Medicine, 11, 2111-2122.
31. Stiratelli, R., Laird, N., and Ware, J. (1984). “Random-Effects Models for Serial Observations With Binary Response,” Biometrics, 40, 961-971.
32. Tang, D., Gnecco, C., and Geller, N. (1989). “An Approximate Likelihood Ratio Test for a Normal Mean Vector With Nonnegative Components With Application to Clinical Trials,” Biometrika, 76, 577-583.
33. Tjur, T. (1982). “A Connection Between Rasch’s Item Analysis Model and a Multiplicative Poisson Model,” Scandinavian Journul of Statistics, 9
34. Zeger, S. L., and Karim, M. R. (1992), “Generalized Linear Models With Random Effects; Salamander Mating Revisited,” Biometrics, 48, 631-644.