Abstract
#Seizures are the most common pediatrics #neurologic disorder in
children who are suffering at least one seizure in the first 16 years of
life. Anyone at any age can have a seizure in certain circumstances,
such as in #meningitis, alcohol withdrawal, and other acute situations
that anyone can experience. Survival analysis typically focuses on time
to event data and recurrent events is a multivariate survival analysis
in which event occurs more than once per subject over follow-up time.
The study of recurrent events data is of particular importance in
medical statistics. The most crucial issue in recurrent data is a
correlation between relapses of each subject. This study aimed to
identify some risks factors of the recurrent times of childhood seizures
by fitting #copula models using a #Bayesian approach. In a retrospective
study, Data of 300 seizure children who had at least one relapse within
the study period has been analyzed. In this study, we modelled the joint
distribution of recurrent events using #parametric copulas within a
Bayesian framework. Results indicated a positive correlation between successive gap-times.
For more articles on BJSTR Journal please click on https://biomedres.us/
Application of Parametric and Nonparametric Copula Marginal Models in Recurrent Failure Times of Childhood Seizures: Bayesian Approach by Mehdi Rahgozar in BJSTR
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