Open Access Research article

Semi-parametric risk prediction models for recurrent cardiovascular events in the LIPID study

Jisheng Cui12*, Andrew Forbes2, Adrienne Kirby3, Ian Marschner34, John Simes3, David Hunt2, Malcolm West5 and Andrew Tonkin2

Author Affiliations

1 World Health Organization Collaborating Centre for Obesity Prevention, Deakin University, Melbourne, Australia

2 Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia

3 NHMRC Clinical Trials Centre, University of Sydney, Sydney, Australia

4 Department of Statistics, Macquarie University, Sydney, Australia

5 Department of Medicine, University of Queensland, Brisbane, Australia

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BMC Medical Research Methodology 2010, 10:27  doi:10.1186/1471-2288-10-27

Published: 1 April 2010



Traditional methods for analyzing clinical and epidemiological cohort study data have been focused on the first occurrence of a health outcome. However, in many situations, recurrent event data are frequently observed. It is inefficient to use methods for the analysis of first events to analyse recurrent event data.


We applied several semi-parametric proportional hazards models to analyze the risk of recurrent myocardial infarction (MI) events based on data from a very large randomized placebo-controlled trial of cholesterol-lowering drug. The backward selection procedure was used to select the significant risk factors in a model. The best fitting model was selected using the log-likelihood ratio test, Akaike Information and Bayesian Information Criteria.


A total of 8557 persons were included in the LIPID study. Risk factors such as age, smoking status, total cholesterol and high density lipoprotein cholesterol levels, qualifying event for the acute coronary syndrome, revascularization, history of stroke or diabetes, angina grade and treatment with pravastatin were significant for development of both first and subsequent MI events. No significant difference was found for the effects of these risk factors between the first and subsequent MI events. The significant risk factors selected in this study were the same as those selected by the parametric conditional frailty model. Estimates of the relative risks and 95% confidence intervals were also similar between these two methods.


Our study shows the usefulness and convenience of the semi-parametric proportional hazards models for the analysis of recurrent event data, especially in estimation of regression coefficients and cumulative risks.