We extend proportional hazards frailty models for lifetime data to allow a negative binomial, Poisson, Geometric or other discrete distribution of the frailty variable. This might represent, for example, the unknown number of flaws in an item under test. Zero frailty corresponds to a limited failure model containing a proportion of units that never fail (long-term survivors). Ways of modifying the model to avoid this are discussed. The models are illustrated on a previously published set of data on failures of printed circuit boards and on new data on breaking strengths of samples of cord
Abstract. In this paper, we propose a proportional hazards model for discrete data analogous to the ...
Frailty models are the survival data analog to regression models, which account for heterogeneity an...
In extending survival models to include frailty effects, the relative merits of parametric and nonpa...
We extend proportional hazards frailty models for lifetime data to allow a negative bi-nomial, Poiss...
The hazard function plays a central role in survival analysis. In a homogeneous population, the dist...
Due to large number of equipment in industrial companies, maintenance management may not always be a...
In survival analysis recurrent event times are often observed on the same subject. These event times...
1 SUMMARY. In survival data analysis, the proportional hazard model was introduced by Cox (1972) in ...
The use of frailty models to account for unobserved individual he terogeneity and other random effec...
Frailty models account for the clustering present in grouped event time data. A proportional hazards...
Abstract. A common way of allowing heterogeneity between individuals in mod-els for lifetime data is...
In this work I consider models for survival data when the assumption of proportionality does not hol...
Frailty models account for the clustering present in grouped event time data. A proportional hazards...
Proportional hazards model is commonly used in survival analysis for estimating the effects of diffe...
A key assumption of the popular Cox model is that the observations in the study are statistically in...
Abstract. In this paper, we propose a proportional hazards model for discrete data analogous to the ...
Frailty models are the survival data analog to regression models, which account for heterogeneity an...
In extending survival models to include frailty effects, the relative merits of parametric and nonpa...
We extend proportional hazards frailty models for lifetime data to allow a negative bi-nomial, Poiss...
The hazard function plays a central role in survival analysis. In a homogeneous population, the dist...
Due to large number of equipment in industrial companies, maintenance management may not always be a...
In survival analysis recurrent event times are often observed on the same subject. These event times...
1 SUMMARY. In survival data analysis, the proportional hazard model was introduced by Cox (1972) in ...
The use of frailty models to account for unobserved individual he terogeneity and other random effec...
Frailty models account for the clustering present in grouped event time data. A proportional hazards...
Abstract. A common way of allowing heterogeneity between individuals in mod-els for lifetime data is...
In this work I consider models for survival data when the assumption of proportionality does not hol...
Frailty models account for the clustering present in grouped event time data. A proportional hazards...
Proportional hazards model is commonly used in survival analysis for estimating the effects of diffe...
A key assumption of the popular Cox model is that the observations in the study are statistically in...
Abstract. In this paper, we propose a proportional hazards model for discrete data analogous to the ...
Frailty models are the survival data analog to regression models, which account for heterogeneity an...
In extending survival models to include frailty effects, the relative merits of parametric and nonpa...