Claggett Brian Lee, McCaw Zachary R, Tian Lu, McMurray John J V, Jhund Pardeep S, Uno Hajime, Pfeffer Marc A, Solomon Scott D, Wei Lee-Jen
Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston.
Insitro, South San Francisco.
NEJM Evid. 2022 Oct;1(10). doi: 10.1056/evidoa2200047. Epub 2022 Jun 30.
Data on the occurrence times of multiple outcomes, reflecting the temporal profile of disease burden/progression, have been used to estimate treatment effects in various recent randomized trials. Most procedures for analyzing these data require specific model assumptions. When the assumptions are not met, the results may be misleading. Robust, model-free procedures for study design and analysis that enable clinically meaningful interpretations are warranted.
For each treatment group, we constructed and summarized the estimated mean cumulative count of events over time by the area under the curve (AUC), which can be interpreted as the mean total event-free time lost from multiple undesirable outcomes. A higher curve, and resulting larger AUC, implies a worse treatment. The treatment effect is quantified by the ratio and/or difference of AUCs. The timing and occurrence of recurrent heart failure hospitalizations (HFHs) and cardiovascular (CV) death from Prospective Comparison of ARNI with ARB Global Outcomes in HF with Preserved Ejection Fraction (PARAGON-HF), comparing sacubitril/valsartan with valsartan, are presented for illustration. We also discuss the design of future studies on the basis of the proposed method.
With 48 months of follow-up, estimated AUCs, representing the total event-free time lost to HFHs and CV death, were 11.3 and 13.1 event-months for sacubitril/valsartan and valsartan, respectively. The ratio of these AUCs was 0.86 (95% confidence interval, 0.75 to 1.00; P=0.049), a 14% reduction of disease burden favoring combination therapy. A future study, similar to PARAGON-HF, designed using the new proposal would require fewer patients would than a conventional time-to-first-event analysis.
The proposed method is robust and model-free and provides a clinically interpretable, time-scale summary of the treatment effect. (Funded by National Institutes of Health.).
反映疾病负担/进展时间特征的多种结局发生时间的数据，已被用于近期各类随机试验中估计治疗效果。多数分析这些数据的方法需要特定的模型假设。当假设不成立时，结果可能会产生误导。因此，需要稳健的、无模型的研究设计和分析方法，以便做出具有临床意义的解释。
对于每个治疗组，我们通过曲线下面积（AUC）构建并总结了随时间估计的事件平均累积计数，其可解释为因多种不良结局而损失的平均无事件总时间。曲线越高，AUC越大，表明治疗效果越差。治疗效果通过AUC的比值和/或差值来量化。以射血分数保留的心力衰竭（HF）患者中ARNI与ARB全球结局的前瞻性比较（PARAGON-HF）研究为例，展示了与缬沙坦相比，沙库巴曲缬沙坦治疗时复发性心力衰竭住院（HFH）和心血管（CV）死亡的时间及发生情况。我们还基于所提出的方法讨论了未来研究的设计。
经过48个月的随访，沙库巴曲缬沙坦组和缬沙坦组因HFH和CV死亡而损失的无事件总时间的估计AUC分别为11.3和13.1事件月。这些AUC的比值为0.86（95%置信区间，0.75至1.00；P = 0.049），表明联合治疗使疾病负担降低了14%。与PARAGON-HF类似，采用新方法设计的未来研究相比传统的首次事件发生时间分析所需患者更少。
所提出的方法稳健且无模型，可提供具有临床可解释性的、时间尺度上的治疗效果总结。（由美国国立卫生研究院资助。）
