A-HIPI model workflow: data-driven risk stratification for personalized treatment. The flowchart illustrates the A-HIPI model workflow, starting with continuous variables like age and lymphocyte count, which are analyzed without arbitrary cutoffs to preserve data integrity. Through statistical modeling, nonlinear relationships are captured to generate risk predictions. Patients are stratified into risk groups using 1 of 3 methods, namely clinical thresholds (predefined cutoffs), deviations from average (comparison to cohort norms), or rank-based stratification (percentile-based grouping). These stratification methods influence the output, predictions of PFS and OS, to guide personalized treatment and clinical trial design. OS, overall survival; PFS, progression-free survival.

A-HIPI model workflow: data-driven risk stratification for personalized treatment. The flowchart illustrates the A-HIPI model workflow, starting with continuous variables like age and lymphocyte count, which are analyzed without arbitrary cutoffs to preserve data integrity. Through statistical modeling, nonlinear relationships are captured to generate risk predictions. Patients are stratified into risk groups using 1 of 3 methods, namely clinical thresholds (predefined cutoffs), deviations from average (comparison to cohort norms), or rank-based stratification (percentile-based grouping). These stratification methods influence the output, predictions of PFS and OS, to guide personalized treatment and clinical trial design. OS, overall survival; PFS, progression-free survival.

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