Semiparametric estimation of a nested random effects model for the analysis of multi-level clustered failure time data

Computational Statistics and Data Analysis

Volumn 53, Issue 11, 2009, Pages 3864-3871

  Shih, Joanna H ^a   Lu, Shou En ^b  

^a NATIONAL CANCER INSTITUTE   (United States)

^b UNIVERSITY OF MEDICINE AND DENTISTRY OF NEW JERSEY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTERING EFFECT; COVARIATES; ESTIMATION PROCEDURES; FAILURE TIME DATA; HAZARD MODELS; MONTE CARLO; MULTI-LEVEL; NON-PARAMETRIC; PARAMETER ESTIMATE; PROPORTIONAL HAZARDS MODEL; RANDOM EFFECTS; RANDOM EFFECTS MODEL; REGRESSION COEFFICIENT; RELATIVE RISKS; SEMI PARAMETRIC ESTIMATION; SURVIVAL DATA; VITAMIN A;

COVARIANCE MATRIX; DISTRIBUTION FUNCTIONS; ESTIMATION; MAXIMUM LIKELIHOOD ESTIMATION; QUALITY ASSURANCE; RISK PERCEPTION;

CLUSTER ANALYSIS;

EID: 65749119611     PISSN: 01679473     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.csda.2009.04.014     Document Type: Article

References (18)

1. Bandeen-Roche K.J., and Liang K.Y. Modelling failure-time associations in data with multiple levels of clustering. Biometrika 83 (1996) 29-39
2. Chernoff H. On the distribution of the likelihood ratio. The Annals of Mathematical Statistics 25 (1954) 573-578
3. Hougaard P. Survival models for heterogeneous populations derived from stable distributions. Biometrika 73 (1986) 387-396
4. Joe H. Parametric families of multivariate distributions with given margins. Journal of Multivariate Analysis 46 (1993) 262-282
5. Katz, J., 1992. Village and household clustering of morbidity and mortality in developing countries. Doctor of Science dissertation. Johns Hopkins University School of Hygiene and Public Health, Baltimore
6. Katz J., Zeger S.L., West Jr. K.P., et al. Clustering of xerophthalmia within households and villages. International Journal of Epidemiology 22 (1993) 709-715
7. Klein J.P. Semiparametric estimation of random effects using the Cox model based on the EM algorithm. Biometrics 48 (1992) 795-806
8. Ma R., Krewski R., and Burnett R.T. Random effects Cox models: A Poisson modelling approach. Biometrika 90 (2003) 157-169
9. Manda S., and Meyer R. Age at first marriage in Malawi: A Bayesian multilevel analysis using a discrete time-to-event model. Journal of the Royal Statistical Society, Series A 168 (2005) 439-455
10. Murphy S.A. Consistency in a proportional hazards model incorporating a random effect. The Annals of Statistics 22 (1994) 712-731
11. Murphy S.A. Asymptotic theory for the frailty model. The Annals of Statistics 23 (1995) 182-198
12. Nielsen G.G., Gill R.D., Andersen P.K., and Srensen T.I.A. A counting process approach to maximum likelihood estimation in frailty models. Scandinavian Journal of Statistics 19 (1992) 25-43
13. Parner E. Asymptotic theory for the correlated gamma-frailty model. The Annals of Statistics 26 (1998) 183-214
14. Rondeau V., Filleul L., and Joly P. Nested frailty models using maximum penalized likelihood estimation. Statistics in Medicine 25 (2006) 4036-4052
15. Rubin D.B. Inference and missing data. Biometrika 63 (1976) 581-590
16. Sastry N. A nested frailty model for survival data, with an application to the study of child survival in northeast Brazil. Journal of the American Statistical Association 92 (1997) 426-435
17. Shih J.H., and Lu S.E. Analysis of failure time data with multi-level clustering, with application to the child vitamin A intervention trial in Nepal. Biometrics 63 (2007) 673-680
18. West Jr. K.P., Pokhrel R.P., Katz J., et al. Efficacy of vitamin A in reducing the preschool childhood mortality in Nepal. Lancet 338 (1991) 67-71