Penalized partial likelihood for frailties and smoothing splines in time to first insemination models for dairy cows

Biometrics

Volumn 60, Issue 3, 2004, Pages 608-614

  Duchateau, Luc ^a   Janssen, Paul ^b  

^a GHENT UNIVERSITY   (Belgium)

^b HASSELT UNIVERSITY   (Belgium)

Author keywords

Dairy cows; Frailty model; Penalized partial likelihood; Smoothing splines; Time to first insemination

Indexed keywords

DAIRIES; FARMS; PHYSIOLOGICAL MODELS; PHYSIOLOGY;

COX MODEL; DAIRY COW; EPIDEMIOLOGICAL STUDIES; FRAILTY MODELS; PARTIAL LIKELIHOODS; PENALIZED PARTIAL LIKELIHOOD; SMOOTHING SPLINE; TIME TO EVENTS; TIME TO FIRST INSEMINATION; TIME-DEPENDENT COVARIATES;

HAZARDS;

PROTEIN;

LIVESTOCK; MODELING;

ARTICLE; COVARIANCE; DAIRY CATTLE; MILK; NONHUMAN; NONLINEAR SYSTEM; STATISTICAL ANALYSIS; STATISTICAL MODEL; TECHNIQUE;

BOS TAURUS;

EID: 4444226539     PISSN: 0006341X     EISSN: None     Source Type: Journal    
DOI: 10.1111/j.0006-341X.2004.00209.x     Document Type: Article

References (18)

1. Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In Second International Symposium on Inference Theory, B. N. Petrov and F. Csáki (eds), 267-281. Budapest: Akadémiai Kiadó.
2. Andersen, P. K., Klein, J. P., Knudsen, K. M., and Tabanera y Palacios, R. (1997). Estimation of variance in Cox's regression model with shared gamma frailties. Biometrics 53, 1475-1484.
3. Cox, D. R. (1972). Regression models and life-tables (with discussion). Journal of the Royal Statistical Society, Series B 34, 187-220.
4. Dijkhuizen, A. A., Huirne, R. B. M., Jalvingh, A. W., and Stelwagen, J. (1997). Economic impact of common health and fertility problems. In Animal Health Economics, Principles and Applications, A. A. Dijkhuizen and R. S. Moore (eds). 41-58. University of Sydney.
5. Duchateau, L., Janssen, P., Lindsey, P., Legrand, C., Nguti, R., and Sylvester, R. (2002). The shared frailty model and the power for heterogeneity tests in multicenter trials. Computational Statistics and Data Analysis 40, 603-620.
6. Ferguson, J. D. and Galligan, D. T. (1999). Veterinary reproductive programs. In Proceedings of the 32nd Annual Conference of the American Association of Bovine Practitioners, 131-137. Nashville, Tenessee.
7. Gray, R. J. (1992). Flexible methods for analyzing survival data using splines, with applications to breast cancer prognosis. Journal of the American Statistical Association 87, 942-951.
8. Henderson, C. R. (1953). Estimation of variance and covariance components. Biometrics 9, 226-252.
9. Hurvich, C. M., Simonoff, J. S., and Tsai, C. (1998). Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion. Journal of the Royal Statistical Society, Series B 60, 271-293.
10. Klein, J. P. (1992). Semiparametric estimation of random effects using the Cox model based on the EM algorithm. Biometrics 48, 795-806.
11. McGilchrist, C. A. (1993). REML estimation for survival models with frailty. Biometrics 49, 221-225.
12. McGilchrist, C. A. and Aisbett, C. W. (1991). Regression with frailty in survival analysis. Biometrics 47, 461-466.
13. Morgan, B. J. T. (1992). Analysis of Quantal Response Data, 63-67. London: Chapman and Hall.
14. Sleeper, L. A. and Harrington, D. P. (1990). Regression splines in the Cox model with application to covariate effects in liver disease. Journal of the American Statistical Association 85, 941-949.
15. Therneau, T. M. and Grambsch, P. M. (2000). Modeling Survival Data. Extending the Cox Model. New York: Springer Verlag.
16. Therneau, T. M., Grambsch, P. M., and Pankratz, V. S. (2003). Penalized survival models and frailty. Journal of Computational and Graphical Statistics 12, 156-175.
17. Verweij, P. J. M. and van Houwelingen, H. C. (1993). Cross-validation in survival analysis. Statistics in Medicine 12, 2305-2314.
18. Wegman, E. J. and Wright, I. W. (1983). Splines in statistics. Journal of the American Statistical Association 78, 351-365.