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. 2010 Apr 1:10:27.
doi: 10.1186/1471-2288-10-27.

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

Jisheng Cui  1 , Andrew Forbes, Adrienne Kirby, Ian Marschner, John Simes, David Hunt, Malcolm West, Andrew Tonkin
Affiliations

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

Jisheng Cui et al. BMC Med Res Methodol. .

Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

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Figures

Figure 1
Figure 1
Estimated baseline cumulative risk of myocardial infarction (MI) during follow-up for a non-smoking patient aged 60 years with total cholesterol level 5.0 mmol/l, HDL cholesterol 1.0 mmol/l, angina grade less than III, with unstable angina as the qualifying acute coronary syndrome and no history of revascularization, stroke or diabetes mellitus before randomization.
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