In:
The International Journal of Biostatistics, Walter de Gruyter GmbH, Vol. 17, No. 2 ( 2021-11-24), p. 295-315
Kurzfassung:
In this article, we model alternately occurring recurrent events and study the effects of covariates on each of the survival times. This is done through the accelerated failure time models, where we use lagged event times to capture the dependence over both the cycles and the two events. However, since the errors of the two regression models are likely to be correlated, we assume a bivariate error distribution. Since most event time distributions do not readily extend to bivariate forms, we take recourse to copula functions to build up the bivariate distributions from the marginals. The model parameters are then estimated using the maximum likelihood method and the properties of the estimators studied. A data on respiratory disease is used to illustrate the technique. A simulation study is also conducted to check for consistency.
Materialart:
Online-Ressource
ISSN:
1557-4679
DOI:
10.1515/ijb-2019-0099
Sprache:
Englisch
Verlag:
Walter de Gruyter GmbH
Publikationsdatum:
2021
ZDB Id:
2239443-6
