Analyzing recurrent events when the history of previous episodes is unknown or not taken into account: proceed with caution

Albert Navarro, Georgina Casanovas, Sergio Alvarado, David Moriña

Research output: Contribution to journalArticleResearchpeer-review

1 Citation (Scopus)

Abstract

© 2016 SESPAS 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
Original languageEnglish
Pages (from-to)227-234
JournalGaceta Sanitaria
Volume31
Issue number3
DOIs
Publication statusPublished - 1 May 2017

Keywords

  • Bias
  • Cohort studies
  • Recurrence
  • Risk assessment
  • Survival analysis

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