title: Parametric conditional frailty models for recurrent cardiovascular events in the lipid study.
abstract: BACKGROUND
  Analysis of recurrent event data is frequently needed in clinical and epidemiological studies. An important issue in such analysis is how to account for the dependence of the events in an individual and any unobserved heterogeneity of the event propensity across individuals.
  
  
  METHODS
  We applied a number of conditional frailty and nonfrailty models in an analysis involving recurrent myocardial infarction events in the Long-Term Intervention with Pravastatin in Ischaemic Disease study. A multiple variable risk prediction model was developed for both males and females.
  
  
  RESULTS
  A Weibull model with a gamma frailty term fitted the data better than other frailty models for each gender. Among nonfrailty models the stratified survival model fitted the data best for each gender. The relative risk estimated by the elapsed time model was close to that estimated by the gap time model. We found that a cholesterol-lowering drug, pravastatin (the intervention being tested in the trial) had significant protective effect against the occurrence of myocardial infarction in men (HR = 0.71, 95% CI 0.60-0.83). However, the treatment effect was not significant in women due to smaller sample size (HR = 0.75, 95% CI 0.51-1.10). There were no significant interactions between the treatment effect and each recurrent MI event (p = 0.24 for men and p = 0.55 for women). The risk of developing an MI event for a male who had an MI event during follow-up was about 3.4 (95% CI 2.6-4.4) times the risk compared with those who did not have an MI event. The corresponding relative risk for a female was about 7.8 (95% CI 4.4-13.6).
  
  
  CONCLUSIONS
  The number of female patients was relatively small compared with their male counterparts, which may result in low statistical power to find real differences in the effect of treatment and other potential risk factors.
  
  
  CONCLUSIONS
  The conditional frailty model suggested that after accounting for all the risk factors in the model, there was still unmeasured heterogeneity of the risk for myocardial infarction, indicating the effect of subject-specific risk factors. These risk prediction models can be used to classify cardiovascular disease patients into different risk categories and may be useful for the most effective targeting of preventive therapies for cardiovascular disease.
authors.affiliation: Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia. Jisheng.Cui@deakin.edu.au
authors.forename: Jisheng
authors.initials: J
authors.lastname: Cui
authors.forename: Andrew
authors.initials: A
authors.lastname: Forbes
authors.forename: Adrienne
authors.initials: A
authors.lastname: Kirby
authors.forename: Ian
authors.initials: I
authors.lastname: Marschner
authors.forename: John
authors.initials: J
authors.lastname: Simes
authors.forename: Malcolm
authors.initials: M
authors.lastname: West
authors.forename: Andrew
authors.initials: A
authors.lastname: Tonkin
mesh_terms: D000328:Adult; D000368:Aged; D015987:Effect Modifier, Epidemiologic; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D019161:Hydroxymethylglutaryl-CoA Reductase Inhibitors / Q000627:therapeutic use; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D009203:Myocardial Infarction / Q000453:epidemiology* / Q000517:prevention & control; D017035:Pravastatin / Q000627:therapeutic use; D011237:Predictive Value of Tests; D012307:Risk Factors; D055502:Secondary Prevention; D012737:Sex Factors; D016019:Survival Analysis
chemical_list: D019161:Hydroxymethylglutaryl-CoA Reductase Inhibitors; D017035:Pravastatin