Joint Models

Joint models are statistical tools that simultaneously analyze related data types, such as a patient’s ongoing health measurements and their time-to-event outcomes (like survival or disease progression). By combining these data sources, joint models better account for how changes in one aspect influence the other, providing more accurate insights. They are especially useful in medical research for understanding how biomarker trajectories relate to patient outcomes over time. This integrated approach improves predictions and helps researchers identify meaningful relationships that might be missed when analyzing data separately.