Cristina Galán will defend her thesis on Thursday, July 24th

The defence will take place at Salón de grados Faculty of Science and Technology of the EHU Leioa Campus at 11:00

Cristina Galán has been a PhD student at Basque Center for Applied Mathematics (BCAM) since September 2021, working within the Applied Statistics group. Her research is focused on statistical analysis in clinical contexts, particularly through the use of joint models for longitudinal and survival data. She explores and propose statistical models that allow the inclusion of patient-reported outcome characteristics through the joint modeling framework. Previously, she obtained a BSc in Mathematics in 2020 and a MSc in Mathematics and Applications in 2021, both from the Autónoma University of Madrid.

Her thesis, titled "Joint modeling with beta-binomial distribution for patient-reported outcomes and survival data" is under the supervision of Dr. Dae-Jin Lee (IE University) and Dr. Josu Najera Zuloaga (EHU). It will be defended on July 24th, 2025, at 11:00 in the Salón de grados, Faculty of Science and Technology of the EHU Leioa Campus at 11:00.

On behalf of all members of BCAM, we would like to wish her all the best for the future, both professionally and personally.

Abstract

This thesis addresses critical methodological gaps in the joint analysis of patient-reported outcomes (PROs) and survival data. PROs, as discrete bounded measures with inherent overdispersion, require specialized statistical treatment that conventional Gaussian-based joint models fail to provide. We develop novel methodological frameworks that properly account for PRO characteristics through beta-binomial distributions, overcoming limitations of existing approaches.

In this work, we propose, explore, and discuss various statistical approaches for joint modeling, from frequentist to bayesian proposals. Our work highlights the advantages of joint models that integrate longitudinal and survival data while emphasizing the importance of choosing appropriate distributions for PRO data.

The proposed methods' performance is evaluated using simulation studies, and comparisons with common approaches in the literature are provided. Additionally, we applied these methods to analyze a study carried out with chronic obstructive pulmonary disease (COPD) patients, where longitudinal tendencies for PRO data collected and their relationship with patients’ mortality are of interest.