以生物标志物为指导的自适应性丰富设计,用于临床试验的临床临床试验值检测,具有时间到事件结果
Kaiyuan Hua1, Hwanhee Hong1, Xiaofei Wang1
1Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, North Carolina, USA.
Journal of biopharmaceutical statistics
|April 20, 2025
概括
这项研究引入了一种新的生物标志物引导的适应性设计,用于使用受限平均存活时间 (RMST) 的临床试验,以有效评估严重疾病的个性化治疗方法. 拟议的方法优化了患者的丰富性和生物标志物值选择,以改善治疗效果分析.
科学领域:
- 临床试验的设计
- 生物统计学 生物统计学
- 个性化医疗是个性化的医疗.
背景情况:
- 生物标志物引导的适应性丰富设计通过专注于生物标志物阳性患者来提高临床试验的效率.
- 时间到事件的结果对于严重疾病至关重要,但使用受限平均存活时间 (RMST) 进行适应性设计的研究是有限的.
- 危险比率方法是标准的,但缺乏RMST的解释性和假设.
研究的目的:
- 提出一种新的两阶段生物标志物引导的适应性受限制平均存活时间 (RMST) 设计.
- 开发最佳生物标志物值和子组识别方法.
- 在适应性丰富试验中提供治疗效果估计,错误率控制,功率和样本大小计算的工具.
主要方法:
- 一个两阶段的自适应性丰富设计,包括生物标志物值检测.
- 开发基于RMST的治疗效果估计器.
- 控制I型错误率,功率分析和样本大小的方法.
- 瘤学试验重新设计和广泛的模拟研究的数值示例.
主要成果:
- 拟议的设计有效地确定了最佳生物标记物值,并丰富了生物标记物阳性子组.
- 这些方法提供了准确的治疗效果估计和强大的统计能力.
- 模拟研究验证了自适应式RMST设计的性能.
结论:
- 开发的生物标志物引导的自适应RMST设计为个性化医学试验提供了一种高效和可解释的方法.
- 这种设计有助于更好地评估严重疾病的生物标志物定义子组中的治疗方法.
- 该方法为未来的瘤学和其他领域的临床试验设计提供了有价值的框架.
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