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Recent developments of Joint Models for longitudinal and survival data using R
Many biomedical follow-up studies aim to investigate the relationship between a repeated measurement, measured with error and time-to-event data. Joint modelling approaches are used to study these two types of data simultaneously to assess the effect of longitudinal biomarkers on time-to-event data. The first joint modelling proposals have been based on a relationship between one single longitudinal biomarker and time-to-event data. Recently, several extensions have been developed for multivariate longitudinal and survival data, for non-linear longitudinal and survival data, to use different association structures between the longitudinal biomarker and patients` survival and for personalized dynamic predictions of time-to-event data. In this talk, we will focus on the recent developments of joint models from both frequentist and Bayesian perspective, with developed R packages.
Speaker: Ipek Guler
I am Ipek Guler born in Turkey in 1986, after finishing my University degree in Gazi University, Ankara, I have started to my master degree in University of Santiago de Compostela, Spain where I have specialized in applied statistical techniques. During my master studies, I had different internships in Clinical Hospital of Santiago de Compostela, IDICHUS and a spin-off company and I focused my research on longitudinal and survival data.
I have worked in several research projects as a researcher in biostatistics group of University of Santiago de Compostela during 6 years, as a statistical consultant in Maimonides Biomedical Research Institute of Córdoba (IMIBIC) for 2 years and also have worked in national Covid registry data with the Spanish Internal Medicine Society (SEMI). I am currently working in Leuven Biostatistics and Statistical Bioinformatics Centre (L-BioStat) KU Leuven.
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Why R? 2021 Conference is the project lead by Why R? Foundation.
The fifth meeting of Central-Eastern-European R enthusiasts.
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