pkpd-clinical-response

RECIST 1.1 clinical response is calculated by evaluating longitudinal tumor measurement data to classify the best overall response, including handling pseudoprogression for solid tumor trials. This skill automates that classification directly from assessment datasets.

Non-compartmental analysis (NCA) for pharmacokinetic data is performed by processing drug concentration-time datasets to derive standard exposure metrics. This skill provides robust functions to calculate these metrics and integrate them with pharmacodynamic outcomes.

Lifelines is used for survival analysis on clinical trial datasets by integrating it with statsmodels and scipy to execute mixed-effects modeling. This combination supports longitudinal biomarker tracking and exposure-response modeling.

Exposure-response relationships are modeled by linking drug exposure metrics like AUC to efficacy outcomes while tracking PD biomarker modulation. This skill identifies the therapeutic window through integrated pharmacokinetic and pharmacodynamic data analysis.

iRECIST criteria are supported alongside standard RECIST 1.1 for clinical response classification in solid tumor trials. This includes handling pseudoprogression to ensure trial-adjacent analyses remain reproducible and compliant with medical guidelines.

Population PK covariate selection and mixed-effects modeling require numpy, pandas, scipy, statsmodels, and lifelines to process clinical trial datasets. These dependencies enable robust pharmacokinetic modeling and survival analysis integration.