23 Mart 2018 Semineri

Bayesian Framework for Parametric Bivariate Accelerated Lifetime Modeling and its Application to Hospital-acquired Infections
Devrim Bilgili
Statistics, University of North Florida

Abstract
Infectious diseases that can be spread directly or indirectly from one person to another are caused by pathogenic microorganisms such as bacteria, viruses, parasites, or fungi. Infectious diseases remain one of the greatest threats to human health and the analysis of infectious disease data is among the most important application of statistics. In this article, we develop Bayesian methodology using parametric bivariate accelerated lifetime model to study dependency between the colonization and infection times for Acinetobacter baumannii bacteria which is leading cause of infection among the hospital infection agents. We also study their associations with covariates such as age, gender, apache score, antibiotics use 3 months before admission and invasive mechanical ventilation use. To account for singularity, we use Singular Bivariate Extreme Value distribution to model residuals in Bivariate Accelerated lifetime model under the fully Bayesian framework. We analyze a censored data related to the colonization and infection collected in five major hospitals in Turkey using our methodology. The data analysis done in this article is for illustration of our proposed method and can be applied to any situation that our model can be used.

Short Bio
Dr. Bilgili graduated from Northern Illinois University in 2009 with Ph.D. in statistics. He worked at Van Andel Research Institute, MI, the USA where he assisted the team checking the quality of microarrays. In his Ph.D. dissertation, he worked in an interdisciplinary area combining statistics and genetics (statistical genetics) to identify the location of disease genes in mice. After his graduation, he started to work at prestigious Mayo Clinic, FL, the USA where his research included gene mapping for the human genome. Specifically, he participated in association and linkage studies for Parkinson Disease.

His research interests include statistical modeling of infectious disease epidemiology and survival analysis/reliability and nanomedicine. Dr. Bilgili is an Associate Professor of Statistics at the University of North Florida, FL, USA and Visiting Associate Professor at TOBB ETU.

Venue
Friday, March 23, 2018 at 4.00 pm in IE03