Lore Zumeta will defend her thesis on February 16th

• The defense will take place in the Lecture Hall of the Faculty of Science and Technology at UPV/EHU (Leioa)

Lore Zumeta Olaskoaga received her Bachelor’s degree in Mathematics from the Euskal Herriko Unibertsitatea / Universidad del País Vasco (UPV/EHU) in 2016 and her Master’s degree in Statistics and Operations Research  from the Universitat Politècnica de Catalunya and Universitat de Barcelona (UPC and UB) in 2018. 

Zumeta joined the Basque Center for Applied Mathematics - BCAM in July 2018, and she is currently working in the Applied Statistics (AS) research group. Her research interests include Survival Analysis, longitudinal data analysis, statistical modelling applied to the fields of Sports Medicine, Epidemiology and Biomedicine and software development

Her thesis “Statistical Modelling for Recurrent Events in Sports Injury Research with Applications to Football Injury Data” will be supervised by Dae-Jin Lee (Universidad IE, Madrid).

The Lecture Hall of the Faculty of Science and Technology at UPV/EHU, Leioa, will host the defense of her thesis on Friday, February 16th at 11 a.m.

On behalf of the BCAM team, we wish Lore the best of luck in defending her thesis!

Abstract

Sports injuries stand as undesirable side effects of athletic participation, carrying serious consequences for athletes' health, their professional careers, and overall team performance. With the growing availability of data, there has been an increasing reliance on statistical models to monitor athletes' health and mitigate the risks of injuries. 

In this dissertation, we present advanced statistical modelling approaches and software tools for sports injury data. Our focus is on the time-varying and recurrent nature of injury occurrences, and we pursue three primary objectives: (a) identifying biomechanical risk factors using variable selection methods and shared frailty Cox models, (b) developing a flexible recurrent time-to-event approach to model the effects of training load on subsequent injuries, and (c) creating dedicated statistical tools through the statistical open-source software \textbf{R}. These objectives are driven by interdisciplinary research, conducted in close collaboration with the Medical Services of Athletic Club, and are motivated by real-world applications. Specifically, the work is based on three distinct data sets: functional screening tests data, external training load data, and web-scraped football injury data. The statistical advancements developed contribute to ongoing efforts in sports injury prevention, providing insights, methodologies, and accessible software implementations for sports medicine practitioners.