LAST UPDATE: MAY 16, 2019
Longitudinal data is obtained when a time-sequence of measurements is made on a response variable for each of a number of subjects in an experimental or observational study. In such cases, individuals usually display a high degree of similarity in responses over time and, thus, classical regression models are inadequate. This course is aimed at giving its attendees insights into the theoretical concepts and practical experience into the models used for analysis of longitudinal data, particularly mixed-effect models. It will provide an introduction to (1) the theoretical foundations of mixed models, (2) a guide to build, examine, interpret and compare mixed-effect models as well as to conduct hypothesis testing and (3) the necessary resources to conduct a wide range of longitudinal analyses. All practical exercises will be conducted in R. Participants are encouraged to bring datasets to the course and apply the principles to their specific areas of research.
All participants must have intermediate proficiency in R programming and have a solid understanding of regression models.
Mayte Suarez-Farinas, PhD is currently an Associate Professor at the Center for Biostatistics and The Department of Genetics and Genomics Science of the Icahn School of Medicine at Mount Sinai, New York. She received a master’s in mathematics from the University of Havana, Cuba and, in 2003, a Ph.D. degree in quantitative analysis from the
Pontifical Catholic University of Rio de Janeiro, Brazil.
Prior to joining Mount Sinai, she was co-director of Biostatistics at the Center for Clinical and Translational Science at the Rockefeller University, where she developed methodologies for data integration across omic studies, and a framework to evaluate drug response at the molecular level in proof of concept studies in inflammatory skin diseases using mixed-effect models and machine learning. Her long terms goals are to develop robust statistical techniques to mine and integrate complex high-throughput data, tailored to specific disease models, with an emphasis on immunological diseases and to develop precision medicine algorithms to predict treatment response and phenotype.
For questions about this course, please contact Deidra McKoy at firstname.lastname@example.org.