Applying Joint Modelling Regression Approaches in Biomedical Data Science

Abstract

In recent years, the technological revolution is allowing the collection of an enormous amount of data of different types, creating enormously complex databases that require the collaboration of statisticians and clinicians to carry out a biomedical study with guarantees, applying the tools of data science. This requires the development of new statistical techniques. This thesis focuses on joint modelling regression models for multivariate responses. Specifically, we study the cases of two and three continuous outcomes, as well as models for longitudinal and survival data. These techniques are applied in three studies of major epidemiological importance: liver damage and survival in COVID-19 patients, perinatal mental health during the COVID-19 pandemic, and the study of thyroid-related hormones.