Visualizing Individual Patient Profiles in the Context of Population Trends with Applications to Pharmacokinetics and Electrocardiograms

<p class="font-claude-response-body">Pharmacokinetic (PK) and electrocardiogram (ECG) measurements from clinical trials provide critical insight into drug behavior and cardiac safety. PK data describe how drug concentrations change over time, while ECG data, particularly heart rate-corrected QT intervals (QTc), indicate cardiac risk. Both data types are longitudinal and are routinely summarized at the population level in ways that can obscure individual patient experiences, such as a subject whose concentration trajectory deviates sharply from the population trend or whose QTc interval exceeds notable thresholds. Alternatively, patient-specific profiles of longitudinal results provide no insight into how the responses compare to the larger population. <p class="font-claude-response-body">This presentation discusses a standardized visualization framework that displays individual patient trajectories alongside population-level summary statistics within a unified graphical display. This framework is implemented as an interactive JMP add-in using the JMP Scripting Language (JSL), with support for annotations to communicate clinically-relevent thresholds or notable observations (through reference lines or markers, respectively) to highlight potential safety concerns for individual patients. Additional functionality allows for the computation of basic PK parameters such as AUC, C_max, and T_max. The integrated visualization enables the rapid review of individual patients using animation through a local data filter, providing greater insight compared to patient- or population-only graphical displays. These findings demonstrate that standardized tools combining individual and population perspectives deliver a more complete and clinically-actionable view of clinical trial data.