Comparison of Regression and Categorical Analysis for Pharmacokinetic Data From Renal Impairment Studies

Abstract

The US Food and Drug Administration 2024 guidance prefers regression analysis over categorical analysis for pharmacokinetic data for studies that assess pharmacokinetics in patients with impaired renal functions. The objective of this study was to compare these two statistical methods for pharmacokinetic data analysis of renal impairment studies. Baseline data from seven renal impairment studies were pooled to estimate the impact of three creatinine-based equations (Cockcroft-Gault, CKD-EPI₂₀₀₉, and absolute CKD-EPI₂₀₀₉) on classification of participants into different renal impairment categories. Retrospective analyses were performed on two renal impairment studies with three distinct analytes (predominantly renally cleared; and predominantly metabolized by hepatic cytochrome P450 enzymes, or by systemic peptidase) using regression or categorical statistical analysis methods and creatine-based equations. While the three equations were highly correlated, the use of a different equation may result in up to 50% of participants being reclassified into different renal impairment groups. Categorical analysis with analysis of variance provided different point estimates and precision of exposure difference for a given renal impairment group based on the equation used. The use of regression analysis without inclusion of data from participants on hemodialysis, as recommended by the Food and Drug Administration, showed most consistent estimate of the relationship between renal impairment and exposure of three analytes. These retrospective analyses support the Food and Drug Administration recommendations of using regression analysis without data from participants on hemodialysis as the primary analysis of data for renal impairment study; and established a modeling strategy for such analysis.