Multivariate clustered data analysis in developmental toxicity studies

Statistica Neerlandica

Volumn 55, Issue 3, 2001, Pages 319-345

  Molenberghs, G ^a   Geys, H ^a  

^a HASSELT UNIVERSITY   (Belgium)

Author keywords

Conditional model; Dose response models; Generalized estimating equations; Marginal model; Maximum likelihood; Pseudo likelihood

Indexed keywords

EID: 0035539286     PISSN: 00390402     EISSN: None     Source Type: Journal    
DOI: 10.1111/1467-9574.00172     Document Type: Article

References (73)

1. AERTS, M. and G. CLAESKENS (1997), Local polynomial estimation in multi-parameter likelihood models, Journal of the American Statistical Association 92, 1536-545.
2. AERTS, M., L. DECLERCK and G. MOLENBERGHS (1997), Likelihood misspecification and safe dose determination for clustered binary data, Environmetrics 8, 613-627.
3. ALTHAM, P. M. E. (1978), Two generalizations of the binomial distribution, Applied Statistics 27, 162-167.
4. ARNOLD, B. C., E. CASTILLO and J.-M. SARABIA (1992), Conditionally specified distributions, Lecture Notes in Statistics 73, New York: Springer-Verlag.
5. ARNOLD, B. C. and D. STRAUSS (1991), Pseudolikelihood estimation: Some examples, Sankhya B 53, 233-243.
6. BAHADUR, R. R. (1961), A representation of the joint distribution of responses to n dichotomous items, in: H. SOLOMON (ed.), Studies in item analysis and prediction, Stanford Mathematical Studies in the Social Sciences VI, Stanford, California, Stanford University Press.
7. BROWN, N. A. and S. FABRO (1981), Quantitation of rat embryonic development in vitro: A morphologhical scoring system, Teratology 24, 65-78.
8. CATALANO, P. J. and L. M. RYAN (1992), Bivariate latent variable models for clustered discrete and continuous outcomes, Journal of the American Statistical Association 87, 651-658.
9. CATALANO, P., L. RYAN and D. SCHARFSTEIN (1994), Modeling fetal death and malformation in developmental toxicity, Risk Analysis 14, 611-619.
10. CATALANO, P. J., D. O. SCHARFSTEIN, L. M. RYAN, C. A. KIMMEL and G. L. KIMMEL (1993), Statistical model for fetal death, fetal weight, and malformation in developmental toxicity studies, Teratology 47, 281-290.
11. CHEN, J. J. and R. L. KODELL (1989), Quantitative risk assessment for teratologic effects, Journal of the American Statistical Association 84, 966-971.
12. CHEN, J. J., R. B. KODELL, R. B. HOWE and D. W. GAYLOR (1991), Analysis of trinomial responses from reproductive and developmental toxicity experiments, Biometrics 47, 1049-1058.
13. CONAWAY, M. (1989), Analysis of repeated categorical measurements with conditional likelihood methods, Journal of the American Statistical Association 84, 53-62.
14. COX, D. R. (1972), The analysis of multivariate binary data, Applied Statistics 21, 113-120.
15. COX, D. R. and N. WERMUTH (1992), Response models for mixed binary and quantitative variables, Biometrika 79, 441-461.
16. COX, D. R. and N. WERMUTH (1994), A note on the quadratic exponential binary distribution, Biometrika 81, 403-408.
17. CRUMP, K. S. and R. B. HOWE (1983), A review of methods for calculating statistical confidence limits in low dose extrapolation, in: D. B. Clayson, D. Krewki and I. Munro (eds.), Toxicological risk assessment, Volume 1, Biological and Statistical Criteria, Boca Raton, CRC Press, 187-203.
18. DALE, J. (1986), Global cross-ratio models for bivariate, discrete, ordered responses, Biometrics 42, 909-917.
19. DECLERCK, L., M. AERTS and G. MOLENBERGHS (1998), Behaviour of the likelihood ratio test statistic under A Bahadur model for exchangeable binary data, Journal of Statistical Computation and Simulation 61, 15-3 38.
20. DIGGLE, P. J., K. Y. LIANG and S. L. ZEGER (1994), Analysis of longitudinal data, Oxford, Oxford University Press.
21. FITZMAURICE, G. M. and N. M. LAIRD, (1995), Regression models for a bivariate discrete and continuous outcome with clustering, Journal of the American Statistical Association 90, 845-852.
22. GEYER, C. J. and E. A. THOMPSON (1992), Constrained Monte Carlo maximum likelihood for dependent data (with discussion), Journal of the Royal Statistical Society Series B, 00, 657-699.
23. GEYS, H., G. MOLENBERGHS and S. R. LIPSITZ (1998), A note on the comparison of pseudo-likelihood and generalized estimating equations for marginal odds ratio models, Journal of Statistical Computation and Simulation 62, 45-72.
24. GEYS, H., G. MOLENBERGHS and L. RYAN (1997), Pseudo-likelihood inference for clustered binary data, Communications in Statistics: Theory and Methods 26, 2743-2767.
25. GEYS, H., G. MOLENBERGHS and L. RYAN (1999), Pseudo-likelihood modelling of multivariate outcomes in developmental toxicology, Journal of the American Statistical Association 94, 734-745.
26. GEYS, H., G. MOLENBERGHS and P. WILLIAMS (2001a), Analysis of toxicology data with covariates specific to each observation, Journal of Agricultural, Biological, and Environmental Statistics 6.
27. GEYS, H., M. REGAN, P. CATALANO, and G. MOLENBERGHS, (2001b), Two latent variable risk assessment approaches for combined continuous and discrete outcomes from developmental toxicity data, submitted.
28. GLONEK, G. F. V and P. MCCULLAGH (1995), Multivariate logistic models, Journal of the Royal Statistical Society B 57, 533-546.
29. HASEMAN, J. K. and L. L. KUPPER (1979), 'Analysis of dichotomous response data from certain toxicological experiments, Biometrics 35, 281-293.
30. HEDEKER, D. and R. D. GIBBONS (1993), MIXOR: a computer program for mixed effects ordinal probit and logistic regression, University of Illinois, Chicago.
31. HOSMER, D. W. and S. LEMESHOW (1989), Applied logistic regression, New York, Wiley.
32. HOSMER, D. W., T. HOSMER, S. LEMESHOW, and S. LE CESSIE (1997), A comparison of goodness-of-fit tests for the logistic regression model, Statistics in Medicine, 16, 965-980.
33. HOUWING-DUISTERMAAT, J. J. and J. C. VAN HOUWELINGEN, (1998), Incorporation of family history in logistic regression models, Statistics in Medicine, 17, 2865-2882.
34. KIMMEL, G. L., P. L. WILLIAMS, C. A. KIMMEL, T. W. CLAGGETT and N. TUDOR (1994), The effects of temperature and duration of exposure on in vitro development and response-surface modelling of their interaction, Teratology 49, 366-367.
35. KIMMEL, C. A. and D. W. GAYLOR (1988), Issues in qualitative and quantitative risk analysis for developmental toxicology, Risk Analysis 8, 15-20.
36. KREWSKI, D. and J. VAN RYZIN (1981), Dose-response models for quantal response toxicity data, in: M. Csorgo, D. Dawson, J. N. K. Rao and E. Saleh (eds.), Statistics and Related Topics, North-Holland, New York, 201-231.
37. KUPPER, L. L. and J. K. HASEMAN (1978), The use of a correlated binomial model for the analysis of certain toxicology experiments, Biometrics 34, 69-76.
38. KUPPER, L. L., C. PORTIER, M. D. HOGAN and E. YAMAMOTO (1986), The impact of litter effects on dose-response modeling in teratology. Biometrics 42, 85-98.
39. LANG, J. B. and A. AGRESTI (1994), Simultaneously modeling of joint and marginal distributions of multivariate categorical responses, Journal of the American Statistical Association 89, 625-632.
40. LE CESSIE, S. and J. C. VAN HOUWELINGEN (1994), Logistic regression for correlated binary data, Applied Statistics 43, 95-108.
41. LE CESSIE, S. and J. C. VAN HOUWELINGEN (1995), Testing the fit of a regression model via score tests in random effects models, Biometrics 51, 600-614.
42. LIANG, K. Y. and S. ZEGER, (1986), Longitudinal data analysis using generalized linear models, Biometrika 73, 13-22.
43. LIANG, K. Y., S. ZEGER and B. QAQISH (1992), Multivariate regression analyses for categorical data, Journal of the Royal Statistical Society B 54, 3-40.
44. LIPSITZ, S. R., G. M. FITZMAURICE and G. MOLENBERGHS ( 1996), Goodness-of-fit tests for ordinal response regression models, Applied Statistics 45, 175-190.
45. LIPSITZ, S.R., N. M. LAIRD and D. P. HARRINGTON (1991), Generalized estimating equations for correlated binary data: using the odds ratio as a measure of association, Biometrika 78, 153-160.
46. MOLENBERGHS, G., L. DECLERCK and M. AERTS (1998), Misspecifying the likelihood for clustered binary data, Computational Statistics and Data Analysis 26, 327-350.
47. MOLENBERGHS, G., H. GEYS and M. BUYSE (1999), Validation of surrogate endpoints in randomized experiments with mixed discrete and continuous outcomes, submitted.
48. MOLENBERGHS, G., H. GEYS, L. DECLERCK, G. CLAESKENS, and M. AERTS (1998), Analysis of clustered multivariate data from developmental toxicity studies in: R. PAYNE, and P. GREEN (eds.), COMPSTAT: Proceeding in computational statistics (keynote paper), Physica Verlag, 3-14.
49. MOLENBERGHS, G. and E. LESAFFRE (1994), Marginal modelling of correlated ordinal data using a multivariate plackett distribution, Journal of the American Statistical Association 89, 633-644.
50. MOLENBERGHS, G. and L. M. RYAN (1999). Likelihood inference for clustered multivariate binary data, Environmetrics 10, 279-300.
51. MOORE, D. S. and M. C. SPRUILL (1975), Unified large-sample theory of general chi-squared statistics for tests of fit, Annals of Statistics 3, 599-616.
52. MORGAN, B. J. T. (1992), Analysis of quantal response data, Chapman and Hall, London.
53. NEUHAUS, J. M. and J. D. KALBFLEISCH (1998), 'Between- and within-cluster covariate effects in the analysis of clustered data, Biometrics 54, 638-645.
54. NEUHAUS, J. M. and N. P. JEWELL (1993), A geometric approach to assess bias due to omitted covariates in generalized linear models, Biometrika 80, 807-815.
55. NEUHAUS, J. M. and M. L. LESPERANCE (1996), Estimation efficiency in a binary mixed-effects model setting, Biometrika 83, 441-446.
56. OCHI, Y. and R. L. PRENTICE (1984), Likelihood inference in a correlated probit regression model, Biometrika 71, 531-543.
57. OLKIN, I. and R. F. TATE, (1961), Multivariate correlation models with mixed discrete and continuous variables, Annals of Mathematical Statistics 32, 448-465 (with correction in 36, 343-344).
58. PLACKETT, R. L. (1965), A class of bivariate distributions, Journal of the American Statistical Association 60, 516-522.
59. PRENTICE, R. L. (1988), Correlated binary regression with covariates specific to each binary observation, Biometrics 44, 1033-1048.
60. PRICE, C. J., C. A. KIMMEL, R. W. TYL and M. C. MARR (1985), The developmental toxicity of ethylene glycol in rats and mice, Toxicology and Applied Pharmacology 81, 113-127.
61. REGAN, M. M. and P. J. CATALANO (1999a), Likelihood models for clustered binary and continuous outcomes: application to developmental toxicology, Biometrics 55, 760-768.
62. REGAN, M. M. and P. J. CATALANO (1999b), Bivariate dose-response modeling and risk estimation in developmental toxicology, Journal of Agricultural, Biological and Environmental Statistics 4, 217-237.
63. ROBERTS, W. C. and C. O. ABERNATHY (1996), Risk assessment: principles and methodologies, in: A. FAN and L. W. CHANG (eds.), Toxicology and risk assessment, principles, methods and applications. New York, Marcel Dekker Inc., 245-270.
64. RYAN, L. (1992), Quantitative risk assessment for developmental toxicity, Biometrics 48, 163-174.
65. RYAN, L. M., P. J. CATALANO, C. A. KIMMEL and G. L. KIMMEL (1991), Relationship between fetal weight and malformation in developmental toxicity studies, Teratology 44, 215-223.
66. SKELLAM, J. G. (1948), A probability distribution derived from the binomial distribution by regarding the probability of a success an variable between the sets of trials, Journal of the Royal Statistical Society. Series B 10, 257-261.
67. TANNER, M. A. (1991), Tools for statistical inference: observed data and data augmentation methods, Berlin, Springer Verlag.
68. TEN HAVE, T. R. and T. HARTZEL (1995), Comparison of two approaches to analyzing correlated binary data in developmental toxicity studies, Teratology 52, 267-276.
69. TEN HAVE, T. R., J. R. LANDIS and S. L. WEAVER (1995), Association models for periodontal disease progression: a comparison of methods for clustered binary data, Statistics in Medicine 14, 413-429.
70. TSIATIS, A. A. (1980), A note on a goodness-of-fit test for the logistic regression model, Biometrika 67, 250-251.
71. TYL, R. W., M. C. PRICE, M. C. MARR and C. A. KIMMEL (1988), Developmental toxicity evaluation of dietary Di(2-ethylhexyl)phthalate in Fisher 344 rats and CD-1 mice, Fundamental and Applied Toxicology 10, 395-412.
72. WILLIAMS, P. L. and L. M. RYAN (1996), Dose-response models for developmental toxicology, in: R. D. HOOD (ed.), Handbook of developmental toxicology, New York, CRC Press, 635-666.
73. ZHU, Y. and K. FUNG (1996), Statistical methods in developmental toxicity risk assessment, in: A. FAN and L. W. CHANG (eds.), Toxicology and risk assessment, principles, methods and applications, New York, Marcel Dekker Inc., 413-446.