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

• Published in: Gaceta sanitaria Vol. 31; no. 3; pp. 227 - 234
• Main Authors: Navarro, Albert, Casanovas, Georgina, Alvarado, Sergio, Moriña, David
• Format: Journal Article
• Language: English
• Published: Spain Elsevier España, S.L.U 01.05.2017; Ediciones Doyma, S.L; Elsevier
• Subjects: Absenteeism; Análisis de supervivencia; Bias; Cohort studies; Computer Simulation; Confidence Intervals; Demography; Estudios de cohortes; Follow-Up Studies; Health Care Sciences & Services; Health Policy & Services; Humans; Incidence; Infectious Diseases; Internal Medicine; Medición del riesgo; Netherlands; Proportional Hazards Models; Psychology; Public, Environmental & Occupational Health; Recurrence; Recurrencia; Risk Assessment; Sample Size; Sesgo; Sociology; Survival analysis; Recurrence; Survival analysis; Recurrencia; Estudios de cohortes; Risk assessment; Bias; Cohort studies; Sesgo; Medición del riesgo; Análisis de supervivencia
• ISSN: 0213-9111; 1578-1283
• DOI: 10.1016/j.gaceta.2016.09.004

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