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.

Opportunities for the control of coronary heart disease in Australia

Objective: To estimate the number of coronary events that could be prevented in Australia each year by the use of preventive and therapeutic strategies targeted to subgroups of the population based on their levels of risk and need. Methods: Estimates of risk reduction from the published literature, prevalence estimates of elevated risk factor levels from the 1995 National Health Survey and treatment levels from the Australian collaborating centres in the World Health Organization's MONICA Project were used to calculate numbers of coronary events preventable among men and women aged 35-79 years in Australia. Results: Approximately 14,000 coronary events could be avoided each year if the mean level of cholesterol in the population was reduced by 0.5 mmol/L, smoking prevalence was halved and prevalence of physical inactivity was reduced to 25%. This represents a reduction in coronary events of about 40%. Even with less optimistic targets, a reduction of 20% could be attained, while the achievement of some internationally recommended targets could lead to almost 50% reduction. In the short term, aggressive medical treatment of people with elevated levels of risk factors and established coronary disease offers the greatest opportunity for reducing coronary events. Conclusion: A comprehensive approach to reduce levels of behavioural and biological risk factors and improve the use of effective treatment could lead to a large reduction in coronary event rates. In the long term, primary prevention - especially to reduce smoking, lower cholesterol levels and increase exercise - has the potential to reduce the population levels of risk and hence contain the national cost of coronary disease.