Capturing Cumulative Disease Burden in Chronic Kidney Disease Outcome Trials: Area Under the Curve and Restricted Mean Time in Favor of Treatment Beyond Conventional Time-to-First Analysis

TL;DR

This paper addresses the limitations of traditional time-to-first-event analyses in chronic kidney disease (CKD) outcome trials by proposing two complementary estimands, area under the curve (AUC) and restricted mean time in favor of treatment (RMT-IF), that quantify cumulative disease burden by integrating event severity and duration in a multistate framework. The methods are applied to the REWIND trial CKD-risk subgroup, revealing that dulaglutide reduces cumulative disease burden by 14.3% (AUC) and increases time in more favorable health states by 0.116 years (RMT-IF) over six years, with death and survival contributing the most to benefit. These approaches provide intuitive, absolute measures of treatment effect and allow decomposition by disease stage, enabling more comprehensive assessment of kidney-protective therapies beyond delaying first events. The findings support using AUC and RMT-IF as robust, policy-relevant tools in CKD trials, with potential utility across other chronic progressive diseases to improve trial design and interpretability.

Abstract

Chronic kidney disease (CKD) affects millions worldwide and progresses irreversibly through stages culminating in end-stage renal disease (ESRD) and death. Outcome trials in CKD traditionally employ time-to-first-event analyses using the Cox models. However, this approach has fundamental limitations for progressive diseases: it assigns equal weight to each composite endpoint component despite clear clinical hierarchy: an eGFR decline threshold receives the same weight as ESRD or death in the analysis, and it captures only the first occurrence while ignoring subsequent progression. Given CKD's gradual evolution over years, comprehensive treatment evaluation requires quantifying cumulative disease burden: integrating both event severity and time spent in each disease state. We propose two complementary approaches to better characterize treatment benefits by incorporating event severity and state occupancy: area under the curve (AUC) and restricted mean time in favor of treatment (RMT-IF). The AUC method assigns ordinal severity scores to disease states and calculates the area under the mean cumulative score curve, quantifying total event-free time lost. Treatment effects are expressed as AUC ratios or differences. The RMT-IF extends restricted mean survival time to multistate processes, measuring average time patients in the treatment arm spend in more favorable states versus the comparator. These methods better capture CKD's progressive nature where treatment benefits extend beyond first-event delay to overall disease trajectory modification. By discriminating between events of differing clinical importance and quantifying the complete disease course, these estimands offer alternative assessment frameworks for kidney-protective therapies, potentially improving efficiency and interpretability of future CKD outcome trials.

Figures (2)

• Figure 1: Cumulative hazard curves for the first occurrence of composite renal outcomes, including death, ESRD, and eGFR declines of $57\%$, $50\%$, and $40\%$ from baseline, by treatment arm in the CKD risk subgroup of the REWIND trial.
• Figure 2: Mean cumulative disease score curves by treatment arm in the CKD risk subgroup of the REWIND trial. Higher scores indicate greater disease burden. The gray dotted line shows the AUC ratio over time.