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PubPredict:在瘤学中预测进展和生存,利用出版物和早期疗效数据进行预测
Jianqi Zhang1, Yufei Guo2, Junyi Zhou1
1Amgen Inc., Thousand Oaks, California, USA.
Pharmaceutical statistics
|July 13, 2023
概括
这项研究使用马尔科夫模型将早期癌症试验的疗效与长期生存结果联系起来. PubPredict工具有助于设计具有有限早期数据的后期试验.
科学领域:
- 在瘤学瘤学.
- 临床试验设计 临床试验设计
- 生物统计学 生物统计学
背景情况:
- 早期瘤学试验经常报告反应率,而不是像无进展生存 (PFS) 或总生存 (OS) 这样的最终生存终点.
- 这种数据缺口使晚期药物开发和试验设计复杂化.
- 从早期的疗效信号预测长期结果对于战略决策至关重要.
研究的目的:
- 在固体瘤中建立早期疗效结果和晚期生存终点之间的定量联系.
- 开发基于早期临床试验数据的无进展生存 (PFS) 和总生存 (OS) 的预测模型.
- 使用早期阶段 (1/2) 疗效数据,创建一个实用工具,为第三阶段试验设计提供建议.
主要方法:
- 利用连续马尔科夫链 (CMC) 模型,根据RECISTv1.1标准模拟固体瘤进展.
- 参数化了CMC模型的过渡强度矩阵,使用已公布的总和总结统计数据.
- 生成模拟主体级事件时间数据,证明与现有研究的良好近似性.
主要成果:
- 开发了一种方法,通过修改CMC模型的过渡强度矩阵来预测PFS和/或OS.
- 该模型结合了对应答率,深度,时间和持续时间的假设,以预测生存率.
- 这款R光的应用程序PubPredict被创建用于实现这些预测算法.
结论:
- 开发的CMC建模方法和PubPredict工具可以量化地将早期疗效信号与长期生存终点联系起来.
- 这种方法为设计第三阶段瘤学试验提供了宝贵的见解,当只有早期阶段数据可用时.
- PubPredict提供可定制的功能,包括响应水平和治疗交叉,提高其在试验规划中的实用性.
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