各位好！今天与大家分享一篇近期发表在CCR上的一篇文献。研究是由FDA-AACR-ASA在基于讨论意见稿和会议的基础上，针对临床试验的总生存期这一关键性指标达成的共识与指导建议。一起来看看FDA视角下，临床试验中针对这一指标的解读与分析应该注意哪些要点。
Improving Collection and Analysis of Overall Survival Data
Lisa R Rodriguez, Nicole J Gormley, Ruixiao Lu, Anup K Amatya, George D Demetri, Keith T Flaherty, Ruben A Mesa, Richard Pazdur, Mikkael A Sekeres, Minghua Shan, Steven Snapinn, Marc R Theoret, Rukiya Umoja, Jonathon Vallejo, Nicholas J H Warren, Qing Xu, Kenneth C Anderson
Clinical Cancer Research 23 July 2024
Advances in anticancer therapies have provided crucial benefits for millions of patients who are living long and fulfilling lives. While these successes should be celebrated, there is certainly room to continue improving cancer care. Increased long-term survival presents additional challenges for determining whether new therapies further extend patients' lives through clinical trials, commonly known as the gold standard endpoint of overall survival (OS). As a result, there is an increasing reliance on earlier efficacy endpoints , which may or may not correlate with OS, to continue the timely pace of translating innovation into novel therapies available for patients. Even when not powered as an efficacy endpoint, OS remains a critical indication of safety for regulatory decisions and is a key aspect of the U.S. Food and Drug Administration's Project Endpoint. Unfortunately, in the pursuit of earlier endpoints, many registrational clinical trials lack adequate planning, collection, and analysis of OS data, which complicates interpretation of a net clinical benefit or harm. This article shares best practices, proposes novel statistical methodologies, and provides detailed recommendations to improve the rigor of using OS data to inform benefit-risk assessments, including incorporating the following in clinical trials intending to demonstrate the safety and effectiveness of a cancer therapy: prospective collection of OS data, establishment of fit-for-purpose definitions of OS detriment, and prespecification of analysis plans for using OS data to evaluate for potential harm. These improvements hold promise to help regulators, patients and providers better understand the benefits and risks of novel therapies.
背景：随机临床试验癌症和白血病B组（CalibGB）140503表明，对于临床分期的T1 N 0非小细胞肺癌（SOC）患者，叶下切除术与叶切除术后的肿瘤学结局相似。对于基于脏层胸腔侵犯（VPI）病理分期为T2的肿瘤患者，肺切除范围与复发和生存率的关系存在争议。
1.众所周知，DFS要转化为OS获益才算得上是抗肿瘤治疗的重大突破。OS会被哪些干扰？背景上可以细读目前JAMA Oncology上面的这篇文章。研究针对新辅助和辅助背景下获得FDA批准的标志性RCT临床试验，详细分析了抗肿瘤试验中的复发后治疗披露的频率是多少，获得最佳复发后治疗的途径是什么？结果并不如大家所想的理想。能过FDA审查，但缺乏复发后的治疗数据披露，个中原因就值得品味了。
2.上细节：
首先本文是FDA-AACR-ASA三大协会基于目前临床试验总体制定的OS专家共识与考量。本号持续跟进，点击段落下图可以闪回当时的ppt。
其实最经典的关于OS解读和考量的研究在肺癌领域当属ADAURA和JCOG0802。回顾当时的设计，都饱含了学术探索的热情和执行的坚韧。但这个FDA的指导性推荐建议后续研究还是得围绕疾病本质和病程将OS纳入试验设计。
其次，很大程度上药物在追求加速审批的过程中，疾病的标准治疗方式也在悄然改变。这也会造成在这个行业内富者田联阡陌，穷者无立锥之地。针对许多传统药的探索性发现往往被轻视、忽视。
第三，为什么需要患者报告的结局，一方面是从生活质量上来为DFS到OS间潜在的获益/损失用数据证实，另一方面是在复杂设计的情况下/更长期的随访时间内能够进一步揭示更详细的数据。
3.肺癌领域的RCT研究中OS的解读是一项技术活，也是容易被大家所诟病的一个点。AEGEAN trials没有OS但也FDA获批也颇具争议，特别是免疫时代多数RCT研究正在等待OS成熟的情况下。
ODAC呢？出来说句话啊，像816一样再在JCO上发一下AEGEAN获批始末及答疑。
在这个方面的拓展阅读可以阅读知名临床研究点评员V哥的网站。
https://www.vkprasadlab.com/publications
目录
1. INTRODUCTION
2. Trial Design and Planning Considerations (Table 1)
3. Prespecified Statistical Analysis Considerations (Table 2)(Figure 1)
4. Post hoc Statistical Analysis Considerations (Table 3)
5. Subgroup Considerations (Table 4)
6. Benefit–Risk Assessment Considerations (Table 5)
7. Conclusion
— 图表汇总—
2. Trial Design and Planning Considerations
Table 1. Trial design considerations.
3. Prespecified Statistical Analysis Considerations
Table 2. Prespecified analysis considerations.
Figure 1. Potential strategies to establish prospective thresholds to rule out harm and inform drug development decision-making when OS data are underpowered for efficacy.
A, Definitions and hypothetical decision paths when using OS data to analyze efficacy or evaluate for harm. When OS is analyzed for efficacy, it is important to rule out an HR of 1 with a robust statistical test, such as excluding 1 with the upper bound of the 95% confidence interval (CI). In traditional analyses of OS, detriment is confirmed when the lower bound of the CI is >1, which continues to be true. Additionally, when OS is not statistically powered for efficacy, one or more prespecified upper bounds of the appropriate CI thresholds >1 can be used to evaluate for a high probability of substantial detriment and inform drug development and benefit–risk decisions. When OS data are mature, failure to exclude hypothetical threshold Y with the upper bound of the CI indicates the possibility that the investigational therapy is harmful to patients relative to the control, and halting drug development may be appropriate. Excluding Y, but failing to exclude X, would suggest additional OS data are needed. Excluding hypothetical threshold X would indicate a low probability of substantial detriment and that proceeding with the drug development process is appropriate if early endpoints are favorable. If a binary decision framework is preferred, a single threshold could be prespecified.
B, Three hypothetical scenarios that highlight how excluding an OS HR threshold X with a 95% CI (red plot area) can be accomplished much earlier than ruling out 1 during a clinical trial. A separate strategy could entail allowing less stringent Type I errors and prespecifying a confidence interval <95% (gray plot area) with which to exclude an OS HR of 1 at an earlier time point. Plot axes are for illustrative purposes and not to scale.
4. Post hoc Statistical Analysis Considerations
Table 3. Post hoc analysis considerations.
5. Subgroup Considerations
Table 4. Subgroup considerations.
6. Benefit–Risk Assessment Considerations
Table 5. Benefit–risk assessment and potential regulatory implications.
