2018 Annual Meeting

Applying Epidemiology Across the Lifespan to Improve Health Care,
Inform Health Policy and Enhance Population Health




Workshop 1

Joint Modeling of Longitudinal and Survival Data: Tools to Evaluate Exposures and Predict Outcome Across the Lifespan (Part I), MSB E-255

Co-Chairs: Eleni-Rosalina Andrinopoulou, PhD, Department of Biostatistics, Erasmus, MC, & Rotterdam Ophthalmic Institute, Rotterdam, The Netherlands, and Rhonda Szczesniak, PhD, Cincinnati Children’s Hospital 



Studies in life course epidemiology often involve different types of outcomes and exposures being collected on individuals, who are followed as early as gestation and onward into later adult life. The data include longitudinally measured responses (e.g., biomarkers), and the time until an event of interest occurs (e.g., death, intervention). In many epidemiologic studies, these outcomes are separately analysed, although it may be of public health interest to study their association while including key exposures. To that end, it is desirable to employ methods that examine the associations of exposures with longitudinal measurement outcomes simultaneously. This method is referred to in the statistical literature as ‘joint modelling of longitudinal and survival data.’ The idea behind joint modelling of longitudinal and survival data is usually to couple linear mixed effects models for longitudinal measurement outcomes and Cox models for censored survival outcomes.



This workshop is aimed at applied epidemiologic researchers and will provide a comprehensive introduction to this modelling framework. During the workshop it will become clear when these models should be used in practice, what are their key assumptions, and how they can be utilized to extract relevant information from the data and for the purposes of prediction.  Recent extensions of these models, motivated by studies of chronic disease epidemiology, will be presented. Emphasis will be given on applications involving data from life course epidemiology, where we will use the package JMbayes in R. After the end of the course participants will be able to define appropriate joint models to answer their research questions of interest.


Brief Biographies:

Eleni-Rosalina Andrinopoulou, PhD received her Doctorate in Biostatistics from Erasmus Medical Center in the Netherlands in 2014 and has studied as a post-doctoral fellow with Dr. Dimitris Rizopoulos in the Department of Biostatistics, where she now has a permanent position. Her research was motivated by joint modeling of longitudinal and survival data arising from heart valve studies. She has received awards for her work in this area, including funding from the International Society for Clinical Biostatistics. Dr. Andrinopoulou collaborates with researchers both locally and abroad on epidemiological studies in cardiovascular and lung diseases. She teaches quantitative research courses regularly through the NIHES MSc Program at Erasmus. She has provided workshops and other extended courses in advanced longitudinal data analysis to numerous fellows and biomedical faculty. Most recently, she gave a statistics seminar at the Institute of Statistics, Biostatistics and Actuarial Sciences in Belgium on joint modeling of longitudinal survival data.  


Rhonda Szczesniak, PhD
is an Associate Professor of Pediatrics in the Division of Biostatistics and Epidemiology at Cincinnati Children’s Hospital and at the University of Cincinnati. Her work focuses on development and application of statistical methods to analyze medical monitoring data as functional data. She collaborates with researchers around the world to improve how large longitudinal databases are utilized to forecast periods of rapid disease progression. Her epidemiologic areas of research focus on chronic lung diseases and disorders with active projects involving the US Cystic Fibrosis Foundation Patient Registry and translation of prediction models into point of care. Other active projects include trans-generational research of diabetes in pregnancy and ambulatory blood pressure monitoring.  

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