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.

翻译：慢性肾脏病（CKD）影响全球数百万人，其病程不可逆地进展，最终导致终末期肾病（ESRD）和死亡。CKD结局试验传统上采用基于Cox模型的首次事件时间分析。然而，该方法对于进展性疾病存在根本性局限：尽管临床重要性存在明确层级（例如，估算肾小球滤过率下降阈值与ESRD或死亡在分析中被赋予相同权重），且仅捕捉首次事件而忽略后续进展。鉴于CKD在数年间逐渐演变的特性，全面的治疗评估需要量化累积疾病负担：整合事件严重性和各疾病状态的持续时间。我们提出两种互补方法，通过纳入事件严重性和状态驻留时间，以更准确表征治疗获益：曲线下面积（AUC）与治疗获益限制平均时间（RMT-IF）。AUC方法为疾病状态分配序数严重性评分，计算平均累积评分曲线下面积，量化损失的无事件总时间。治疗效果以AUC比值或差值表示。RMT-IF将限制平均生存时间扩展至多状态过程，测量治疗组患者相较于对照组处于更有利状态的平均时间。这些方法能更好地捕捉CKD的进展性本质，其中治疗获益不仅延迟首次事件发生，更改变整体疾病轨迹。通过区分不同临床重要性的事件并量化完整疾病进程，这些估计量为肾脏保护疗法提供了替代评估框架，有望提升未来CKD结局试验的效能与可解释性。