Long-term survivor mixture model with random effects: application to a multi-centre clinical trial of carcinoma

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 environ mental 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 (C) 2001 John Wiley & Sons, Ltd.

Increasing relevance of pharmacogenetics of drug metabolism in clinical practice

Much of the individual variation in drug response is due to genetic drug metabolic polymorphisms. Clinically relevant examples include acetylator status; cytochrome P450 2D6, 2C9 and 2C19 polymorphisms; and thiopurine methyltransferase deficiency. It is important to be aware of which drugs are subject to pharmacogenetic variability. In the future, population-based pharmacogenetic testing will allow more individualized drug treatment and will avoid the current empiricism.