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

Cui, Jisheng, Forbes, Andrew, Kirby, Adrienne, Marschner, Ian, Simes, John, Hunt, David, West, Malcolm and Tonkin, Andrew (2010) Semi-parametric risk prediction models for recurrent cardiovascular events in the LIPID study. BMC Medical Research Methodology, 10 27: 1-9. doi:10.1186/1471-2288-10-27


Author Cui, Jisheng
Forbes, Andrew
Kirby, Adrienne
Marschner, Ian
Simes, John
Hunt, David
West, Malcolm
Tonkin, Andrew
Title Semi-parametric risk prediction models for recurrent cardiovascular events in the LIPID study
Journal name BMC Medical Research Methodology   Check publisher's open access policy
ISSN 1471-2288
Publication date 2010-04-01
Year available 2010
Sub-type Article (original research)
DOI 10.1186/1471-2288-10-27
Open Access Status DOI
Volume 10
Issue 27
Start page 1
End page 9
Total pages 9
Editor Melissa Norton
Place of publication London, U.K.
Publisher BioMed Central
Language eng
Formatted 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.
© 2010 Cui et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Keyword Failure time data
Long-term intervention
Regression-analysis
Nonparametric-estimation
Terminating event
Marginal analysis
Ischemic disease
Angina-pectoris
Clinical-trials
Multiple events
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2011 Collection
School of Medicine Publications
 
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Created: Sun, 09 May 2010, 10:05:47 EST