The trend-renewal process: a useful model for medical recurrence data.
Stat Med
; 32(1): 142-52, 2013 Jan 15.
Article
en En
| MEDLINE
| ID: mdl-22807089
Time-to-event data analysis has a long tradition in applied statistics. Many models have been developed for data where each subject or observation unit experiences at most one event during its life. In contrast, in some applications, the subjects may experience more than one event. Recurrent events appear in science, medicine, economy, and technology. Often the events are followed by a repair action in reliability or a treatment in life science. A model to deal with recurrent event times for incomplete repair of technical systems is the trend-renewal process. It is composed of a trend and a renewal component. In the present paper, we use a Weibull process for both of these components. The model is extended to include a Cox type covariate term to account for observed heterogeneity. A further extension includes random effects to account for unobserved heterogeneity. We fit the suggested version of the trend-renewal process to a data set of hospital readmission times of colon cancer patients to illustrate the method for application to clinical data.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Modelos de Riesgos Proporcionales
/
Interpretación Estadística de Datos
Tipo de estudio:
Prognostic_studies
Límite:
Female
/
Humans
/
Male
Idioma:
En
Revista:
Stat Med
Año:
2013
Tipo del documento:
Article
País de afiliación:
Alemania