In the analysis of survival data wit& parametric models, i t is well known that the Weibull model is not suitable for modeling where the hazard rate ie non-monotonic. For moh c a m , loglogistic model is frequently used. However, due to the symmetric property of the log-logistic model, i t may be po
INTERVAL CENSORED SURVIVAL DATA: A GENERALIZED LINEAR MODELLING APPROACH
โ Scribed by C. P. FARRINGTON
- Publisher
- John Wiley and Sons
- Year
- 1996
- Tongue
- English
- Weight
- 585 KB
- Volume
- 15
- Category
- Article
- ISSN
- 0277-6715
No coin nor oath required. For personal study only.
โฆ Synopsis
A method is described for weak parametric modelling of arbitrarily interval censored survival data using generalized linear models. The method makes use of an associated Bernoulli model, with standard errors based on the observed information matrix. Three types of models are discussed additive and multiplicative hazard models with piecewise constant baseline hazard, and a proportional hazards model with discrete baseline survivor function. These models may be fitted in the statistical package GLIM.
๐ SIMILAR VOLUMES
As the incidence of tuberculosis (TB) has increased in the United States, occupationally acquired TB has increased among the health care workers (HCWs). This paper describes a model developed in response to the needs of an outbreak of multidrug-resistant TB. One of the goals of the outbreak investig
The authors consider the estimation of regression parameters in the context of a class of generalized pmportional hazards models, termed linear transformation models, in the presence of interval-censored data. They present an estimating equation appach whose good performance is demonstrated through
We develop parametric methods for analysing interval-censored data when examination and survival times are not independent. The hazard function is modelled by introducing individual frailties related to the frequency of examinations. Model parameters may be obtained by direct maximization of the mar
We consider the problem of estimation of a joint distribution function of a multivariate random vector with interval-censored data. The generalized maximum likelihood estimator of the distribution function is studied and its consistency and asymptotic normality are established under the case 2 multi