Mixture cure model with random effects for clustered interval-censored survival data.
Stat Med
; 30(9): 995-1006, 2011 Apr 30.
Article
em En
| MEDLINE
| ID: mdl-21472759
ABSTRACT
The mixture cure model is an effective tool for analysis of survival data with a cure fraction. This approach integrates the logistic regression model for the proportion of cured subjects and the survival model (either the Cox proportional hazards or accelerated failure time model) for uncured subjects. Methods based on the mixture cure model have been extensively investigated in the literature for data with exact failure/censoring times. In this paper, we propose a mixture cure modeling procedure for analyzing clustered and interval-censored survival time data by incorporating random effects in both the logistic regression and PH regression components. Under the generalized linear mixed model framework, we develop the REML estimation for the parameters, as well as an iterative algorithm for estimation of the survival function for interval-censored data. The estimation procedure is implemented via an EM algorithm. A simulation study is conducted to evaluate the performance of the proposed method in various practical situations. To demonstrate its usefulness, we apply the proposed method to analyze the interval-censored relapse time data from a smoking cessation study whose subjects were recruited from 51 zip code regions in the southeastern corner of Minnesota.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Análise de Sobrevida
/
Ensaios Clínicos como Assunto
/
Modelos Estatísticos
Tipo de estudo:
Clinical_trials
/
Risk_factors_studies
Limite:
Humans
País/Região como assunto:
America do norte
Idioma:
En
Revista:
Stat Med
Ano de publicação:
2011
Tipo de documento:
Article
País de afiliação:
Singapura