Premium
Long‐term survivor mixture model with random effects: application to a multi‐centre clinical trial of carcinoma
Author(s) -
Yau Kelvin K. W.,
Ng Angus S. K.
Publication year - 2001
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.932
Subject(s) - random effects model , term (time) , mixed model , biostatistics , mixture model , accelerated failure time model , multivariate statistics , survival analysis , statistics , econometrics , generalized linear mixed model , clinical trial , event (particle physics) , computer science , medicine , mathematics , epidemiology , meta analysis , physics , quantum mechanics
A mixture model incorporating long‐term survivors has been adopted in the field of biostatistics where some individuals may never experience the failure event under study. The surviving fractions may be considered as cured. In most applications, the survival times are assumed to be independent. However, when the survival data are obtained from a multi‐centre clinical trial, it is conceived that the environmental conditions and facilities shared within clinic affects the proportion cured as well as the failure risk for the uncured individuals. It necessitates a long‐term survivor mixture model with random effects. In this paper, the long‐term survivor mixture model is extended for the analysis of multivariate failure time data using the generalized linear mixed model (GLMM) approach. The proposed model is applied to analyse a numerical data set from a multi‐centre clinical trial of carcinoma as an illustration. Some simulation experiments are performed to assess the applicability of the model based on the average biases of the estimates formed. Copyright © 2001 John Wiley & Sons, Ltd.