During the last decade the Johns Hopkins Division of Rheumatology has prioritized the development of longitudinal patient cohorts across seven disease-specific Centers of Excellence, with comprehensive clinical, diagnostic, lab, patient-reported, and imaging data, and biospecimens to facilitate discovery. These datasets have now matured to allow greater insight into how multiple factors influence the development and progression of rheumatic diseases and variation in treatment responses. Statistical methods to evaluate and interpret longitudinal data have advanced substantially in recent years, especially for identifying relevant subgroups of patients and predicting trajectories of disease progression and responses to therapy. Many of these methods were developed by members of our team and are only just beginning to be applied to medicine.
The Specific Aims of the Data Science Core are to: 1) Provide data analytical support throughout the research process that will enable investigators to generate, manage, analyze, and interpret data using modern statistical methods; 2) Develop and apply Bayesian hierarchical models (BHMs) to longitudinal cohorts bringing together with diverse sources of data to identify patient subsets predictive of different trajectories of outcomes and responses to treatments; and 3) Apply modern analytic approaches to observational and experimental studies that rigorously address heterogeneity among individuals in their responses to treatments. The Data Science Core will be led by Dr. Scott Zeger who pioneered advances in longitudinal data analysis methods, and leads the transformative Hopkins inHealth Initiative. The Data Science Core will optimize its impact and maximize productivity and efficiency by embedding biomedical data science faculty within each of our Centers of Excellence. We are confident that by working together, our clinician scientists and biomedical data scientists (biostatisticians) can accelerate the pace of research discoveries by analyzing our rich datasets carefully collected over time with new advances in statistical modeling to identify multiple factors that influence the onset and course of rheumatic diseases and better predict individual responses to different treatments that in turn can help guide evidence-based clinical practice.
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