Recurrent Events Model Application in Determining the Risk Factors of Bipolar Disorder Recurrence.

Recurrent events data is one of the most important types of survival data whose main feature is correlation between individual's observations. The aim of this study was to analyze the time to bipolar disorder (BD) relapse and determine the related factors using recurrent events models. In this retrospective study, records of 104 BD patients with at least one relapse who were admitted for the first time (2001-2015) in Farabi hospital of Kermanshah were gathered to identify the factors influencing the time intervals between the recurrent survivals data using the Cox model with and without frailty (shared frailty), once with frailty gamma distribution and once with log-normal distribution frailty. All calculations were performed using R and SPSS software, versions 3.0.2 and 16 and the level of significance was considered at 0.05. Among the employed models, Cox model with lognormal shared frailty showed better fit for BD recurrent survival data. According to results of Cox model with lognormal frailty, 2 factors (marital status and history of veteran) were identified to affect the time intervals between relapses. Because of the better fit of the models with the frailty effect on data, the correlation between the recurrent time intervals of each subject's relapse of BD was confirmed. Also, since the risk of subsequent relapses was less in married and veteran patients, marriage and emotional care supports can be considered as effective factors in reducing the risk of subsequent relapses of this disease.