title: Comparison of risks of cardiovascular events in the elderly using standard survival analysis and multiple-events and recurrent-events methods.
abstract: BACKGROUND
  Epidemiological studies about cardiovascular diseases often rely on methods based on time-to-first-event for data analysis. Without taking into account multiple event-types and the recurrency of a specific cardiovascular event, this approach may underestimate the overall cardiovascular burden of some risk factors, if that is the goal of the study.
  
  
  METHODS
  In this study we compare four different statistical approaches, all based on the Weibull distribution family of survival model, in analyzing cardiovascular risk factors. We use data from the Cardiovascular Health Study as illustration. The four models respectively are time-to-first-event only, recurrent-events only, multiple-event-types only, and joint recurrent and multiple-event-type models.
  
  
  RESULTS
  Although the four models produce consistent results regarding the significance of the risk factors, the magnitude of the hazard ratios and their confidence intervals are different. The joint model produces hazard ratios that are substantially higher than the time-to-first-event model especially for the risk factors of smoking and diabetes.
  
  
  CONCLUSIONS
  Our findings suggest that for people with diabetes and are currently smoking, the overall cardiovascular burden of these risk factors would be substantially higher than that estimated using time-to-first-event method.
authors.affiliation: Department of Biostatistical Sciences, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC, 27157, USA. eip@wakehealth.edu.
authors.forename: Edward H
authors.initials: EH
authors.lastname: Ip
authors.affiliation: Programs of Statistics, Department of Mathematics, Universitas Brawijaya, Malang, 65145, Indonesia. a_efendi@staff.ub.ac.id.
authors.forename: Achmad
authors.initials: A
authors.lastname: Efendi
authors.affiliation: I-BioStat, Universiteit Hasselt & KU Leuven, Hasselt, Belgium. geert.molenberghs@uhasselt.be.
authors.forename: Geert
authors.initials: G
authors.lastname: Molenberghs
authors.affiliation: Department of Epidemiology and Prevention, Wake Forest School of Medicine, Winston-Salem, USA. abertoni@wakehealth.edu.
authors.forename: Alain G
authors.initials: AG
authors.lastname: Bertoni
mesh_terms: D000368:Aged; D000465:Algorithms; D002318:Cardiovascular Diseases / Q000453:epidemiology*; D006801:Humans; D015233:Models, Statistical; D015999:Multivariate Analysis; D016016:Proportional Hazards Models; D011446:Prospective Studies; D012008:Recurrence; D018570:Risk Assessment / Q000379:methods / Q000706:statistics & numerical data*; D012307:Risk Factors; D016019:Survival Analysis; D014481:United States / Q000453:epidemiology