临床试验越来越复杂:机器学习分析了来自16000多项试验的数据
Nigel Markey1, Ben Howitt1, Ilyass El-Mansouri2
1Boston Consulting Group, 80 Charlotte Street, London, W1T 4DF, UK.
Scientific reports
|February 12, 2024
概括
临床试验越来越复杂,时间越来越长,成功率也越来越低. 一个新的试验复杂度得分,来自于机器学习分析超过16000个试验,量化了这一趋势.
科学领域:
- 临床试验的管理管理.
- 监管科学是一种监管科学.
- 医疗信息学 医疗信息学
背景情况:
- 由于创新,监管变化和外部压力,临床试验面临越来越多的复杂性.
- 尽管有进展,但临床试验的时间表已经延长,成功率仍然很低.
- 这引发了关于临床试验复杂性的必要性和影响的问题.
研究的目的:
- 使用大规模数据集分析临床试验的复杂性.
- 开发一个量化指标来评估临床试验的复杂性.
- 调查不同阶段和治疗领域的试验复杂性的趋势.
主要方法:
- 分析了来自16000多个临床试验的协议和数据.
- 应用机器学习算法来评估试验特征 (例如,终点,包含-排除标准).
- 使用回归分析开发试验复杂性评分,将复杂性与试验持续时间关联起来.
主要成果:
- 试验复杂性得分表明,随着时间的推移,临床试验复杂性在各个阶段和治疗领域大幅增加.
- 开发的得分显示了与临床试验总体持续时间的相关性.
- 确定了导致试验复杂性的关键特征.
结论:
- 临床试验显然变得越来越复杂.
- 区分必要的 ("好") 和不必要的 ("坏") 复杂性至关重要.
- 建议探索减少临床试验设计和执行中不必要的复杂性的机制.
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