基于现场一级的历史数据,在投资组合规划阶段对临床试验的注册预测
Sheng Zhong1, Yunzhao Xing1, Mengjia Yu1
1Statistical Innovation Group, Data and Statistical Sciences, AbbVie Inc., North Chicago, Illinois, USA.
Pharmaceutical statistics
|October 23, 2023
概括
准确的临床试验招生预测至关重要. 一种使用一般化的线性混合效应模型和非均的波桑过程的新贝叶斯方法显著提高了比传统方法更准确的预测准确性.
科学领域:
- 临床试验操作临床试验操作
- 统计建模 统计建模
- 制药研究 制药研究
背景情况:
- 准确的临床试验招生预测对于战略规划和运营效率至关重要.
- 使用平均入学率或使用有限的网站激活数据的简单Poisson-Gamma模型的传统方法缺乏精度.
- 早期的投资组合规划往往缺乏用户提供的详细网站信息,阻碍了准确的预测.
研究的目的:
- 开发和验证一种新的统计框架,用于对临床试验启动,试点激活和受试者招募的顺序建模.
- 提高临床试验招生时间线预测在规划阶段的准确性.
- 为数据变化和预测间隔提供强大的建模方法.
主要方法:
- 在贝叶斯框架内利用了通用的线性混合效应模型和非均的波桑过程.
- 模拟国家启动,网站激活和主题入学顺序.
- 在四个治疗领域使用25项预先选择的临床研究数据验证了框架.
主要成果:
- 与传统的统计方法相比,拟议的建模框架在预测准确度方面取得了实质性的改进.
- 该方法有效校准了数据的变化,并为预测间隔提供了准确的覆盖率.
- 生成预测的入学曲线与信心带,提供一个更清晰的视图的潜在时间表.
结论:
- 新的贝叶斯框架在临床试验招生预测准确度方面取得了重大进展.
- 这种系统的方法提高了早期战略和运营规划的可靠性.
- 该方法为可视化预测的入学轨迹和相关的不确定性提供了宝贵的工具.
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