title: Analyzing recurrent events when the history of previous episodes is unknown or not taken into account: proceed with caution.
abstract: OBJECTIVE
  Researchers in public health are often interested in examining the effect of several exposures on the incidence of a recurrent event. The aim of the present study is to assess how well the common-baseline hazard models perform to estimate the effect of multiple exposures on the hazard of presenting an episode of a recurrent event, in presence of event dependence and when the history of prior-episodes is unknown or is not taken into account.
  
  
  METHODS
  Through a comprehensive simulation study, using specific-baseline hazard models as the reference, we evaluate the performance of common-baseline hazard models by means of several criteria: bias, mean squared error, coverage, confidence intervals mean length and compliance with the assumption of proportional hazards.
  
  
  RESULTS
  Results indicate that the bias worsen as event dependence increases, leading to a considerable overestimation of the exposure effect; coverage levels and compliance with the proportional hazards assumption are low or extremely low, worsening with increasing event dependence, effects to be estimated, and sample sizes.
  
  
  CONCLUSIONS
  Common-baseline hazard models cannot be recommended when we analyse recurrent events in the presence of event dependence. It is important to have access to the history of prior-episodes per subject, it can permit to obtain better estimations of the effects of the exposures.
authors.affiliation: GRAAL-Unitat de Bioestadística, Facultat de Medicina, Universitat Autònoma de Barcelona, Bellaterra, Barcelona, Spain. Electronic address: albert.navarro@uab.cat.
authors.forename: Albert
authors.initials: A
authors.lastname: Navarro
authors.affiliation: GRAAL-Unitat de Bioestadística, Facultat de Medicina, Universitat Autònoma de Barcelona, Bellaterra, Barcelona, Spain.
authors.forename: Georgina
authors.initials: G
authors.lastname: Casanovas
authors.affiliation: Programa de Salud Ambiental, Escuela de Salud Pública, Facultad de Medicina, Universidad de Chile, Chile; Facultad de Ciencias de la Salud, Universidad de Tarapacá, Arica, Chile.
authors.forename: Sergio
authors.initials: S
authors.lastname: Alvarado
authors.affiliation: GRAAL-Unitat de Bioestadística, Facultat de Medicina, Universitat Autònoma de Barcelona, Bellaterra, Barcelona, Spain; Unit of Infections and Cancer (UNIC), Cancer Epidemiology Research Program (CERP), Catalan Institute of Oncology (ICO)-IDIBELL, Barcelona, Spain.
authors.forename: David
authors.initials: D
authors.lastname: Moriña
mesh_terms: D000041:Absenteeism; D003198:Computer Simulation; D016001:Confidence Intervals; D005500:Follow-Up Studies; D006801:Humans; D015994:Incidence; D009426:Netherlands; D016016:Proportional Hazards Models; D018570:Risk Assessment; D018401:Sample Size
keywords: Análisis de supervivencia; Bias; Cohort studies; Estudios de cohortes; Medición del riesgo; Recurrence; Recurrencia; Risk assessment; Sesgo; Survival analysis