基于证据的先验评估瘤学第三阶段随机试验的治疗效果
Alexander D Sherry1, Pavlos Msaouel2,3, Gabrielle S Kupferman1
1Department of Radiation Oncology, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX.
JCO precision oncology
|September 30, 2024
概括
使用P值解释III期瘤学试验是很困难的. 一个新的工具计算了治疗效益的概率,为瘤学家提供了更直观的临床试验结果理解.
科学领域:
- 临床瘤学临床瘤学
- 生物统计学 生物统计学
- 医疗信息学 医疗信息学
背景情况:
- 第三阶段瘤学试验通常依赖于P值值来解释结果,这可能是具有挑战性的.
- 治疗有益的概率更直观,但通常不会在试验结果中提供.
- 现有的解释临床试验数据的方法可能无法完全捕捉治疗疗效的细微差别.
研究的目的:
- 开发和发布一个用户友好的工具,用于计算瘤学试验中治疗效益的概率.
- 为了更直观地解释超出P值值的III期瘤学试验结果.
- 帮助瘤学界了解治疗疗效,使用总结统计数据.
主要方法:
- 策划了415个III期随机瘤学试验 (338,600名患者,2004-2020年) 和23,551个科克兰数据库试验.
- 开发了基于观察到的z-score的治疗效应的第三阶段先前概率分布.
- 计算了临床上有意义益处的概率 (危险比率[HR]<0.8) 并比较了试验功率.
主要成果:
- 第三阶段瘤学试验的信号噪声比率高于科克伦试验,但中位数功率仅为49%.
- 65%的试验具有<80%的功率;只有53%的声称优势的试验具有≥90%的有意义益处的概率.
- 在被认为没有益处的试验中,17%的试验实际上具有>90%的实验臂优势概率 (HR <1).
结论:
- 一个实用的,用户友好的工具可以从总结统计数据中计算治疗效应的上下文概率.
- 该工具增强了对更广泛的瘤学社区的第三阶段瘤学试验结果的解释.
- 与传统的P值相比,受益概率提供了一个更直观,更可靠的治疗疗效衡量方法.
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