Background: Composite endpoints in cardiovascular trials combine heterogeneous outcomes-mortality, nonfatal events, hospitalizations, and biomarkers-yet conventional analytical methods sacrifice information by targeting a single dimension. Cox time-to-first-event ignores post-first-event data; Win Ratio discards tied pairs; negative binomial regression treats death as noninformative censoring. Methods: We propose CWOT-CE: a Choquet integral-based composite endpoint analysis that encodes K = 6 outcome dimensions-survival, event-free time, AUC recurrent burden, last event time, biomarker, and alive status-and aggregates them through a non-additive fuzzy measure with pairwise interaction terms. The recurrent event process is represented as two complementary scalar summaries: the area under the cumulative count curve (AUC burden) and the last event time. Inference is via permutation test with exact finite-sample Type I error control and dual confidence interval by inversion. We conducted a simulation study comparing CWOT-CE against Cox TTFE, Win Ratio (WRrec), and WLW across 20 clinically motivated scenarios (1,000-5,000 replications). Results: Under the sharp null (5,000 replications), all methods maintained nominal Type I error (CWOT-CE: 4.8%, MCSE 0.3%). Across 17 non-null scenarios, CWOT-CE outperformed Cox TTFE in 15 (mean +28.8 pp), WLW in 14 (mean +27.2 pp), and Win Ratio in 10, with 5 ties and only 2 narrow losses (mean +5.6 pp). CWOT-CE showed particular advantages in high-correlation settings (+35.4 pp vs. WR), mortality-driven effects (+10.7 pp), and balanced multi-component effects (+10.1 pp). Shapley decomposition correctly identified effect-bearing components across all calibration scenarios. Conclusions: CWOT-CE with block recurrent encoding is broadly effective across clinically relevant scenarios while offering unique interpretive advantages through component attribution.

翻译：背景：心血管试验中的复合终点整合了异质性结局——死亡率、非致命事件、住院事件及生物标志物——但传统分析方法因聚焦单一维度而损失信息。Cox首次事件时间分析忽略首次事件后数据；胜率比丢弃配对平局；负二项回归将死亡视为非信息删失。方法：我们提出CWOT-CE：一种基于Choquet积分的复合终点分析方法，该法编码K=6个结局维度——生存期、无事件时间、复发负担曲线下面积、末次事件时间、生物标志物及存活状态——并通过含成对交互项的非可加模糊测度进行聚合。复发事件过程以两种互补标量摘要表示：累积计数曲线下面积（AUC负担）与末次事件时间。推论基于置换检验，通过逆推法实现精确有限样本I类错误控制与双重置信区间。本研究在20个临床驱动场景（1,000-5,000次重复）下，比较了CWOT-CE与Cox TTFE、胜率比（WRrec）及WLW方法的性能。结果：在严格零假设下（5,000次重复），所有方法均维持名义I类错误率（CWOT-CE：4.8%，MCSE 0.3%）。在17个非零假设场景中，CWOT-CE在15个场景优于Cox TTFE（平均+28.8个百分点）、14个场景优于WLW（平均+27.2个百分点）、10个场景优于胜率比，出现5次持平结果及仅2次微弱劣势（平均+5.6个百分点）。CWOT-CE在高相关性场景（较WR提升+35.4个百分点）、死亡率驱动效应（+10.7个百分点）及平衡多组分效应（+10.1个百分点）中展现显著优势。沙普利分解在所有校准场景中均正确识别效应承载组分。结论：采用块循环编码的CWOT-CE在临床相关场景中广泛有效，并通过组分归因提供独特的解释性优势。