在计划阶段对临床试验的注册预测,使用研究水平的历史数据
Mengjia Yu1, Sheng Zhong2, Yunzhao Xing1
1Statistical Innovation Group, Data and Statistical Sciences, AbbVie Inc., 1 N Waukegan Rd, North Chicago, IL, USA.
Therapeutic innovation & regulatory science
|September 15, 2023
概括
准确的临床试验招生预测至关重要. 一个新的两段统计模型通过考虑时间和地点来提高预测准确性,优于传统方法以更好地规划试验.
科学领域:
- 临床试验的管理管理.
- 在医疗保健中的统计建模.
- 药物经济学 药物经济学
背景情况:
- 准确的临床试验招生时间表预测对于战略决策和运营效率至关重要.
- 像天真平均或简单的波桑-马模型这样的传统方法未能结合关键的时间和位置依赖因素.
- 现有的方法缺乏处理现代临床试验需求的复杂性所需的复杂性.
研究的目的:
- 引入一种新,准确和高效的两部分统计方法,用于临床试验招生时间线预测.
- 开发一个预测模型,将研究层面和历史组织数据集成在一起,用于预测未来的入学率.
- 通过使用现实世界的临床试验数据,与已建立的框架对拟议模型的性能进行验证.
主要方法:
- 一种两段统计方法,使用准松回归来计算主体积累率.
- 一个Poisson过程模型用于模拟受试者注册和网站激活动态.
- 将公开可访问的研究数据与内部历史数据集成,以提高预测能力.
主要成果:
- 与天真和传统的统计方法相比,提出的模型显示出更高的准确性和效率.
- 七项精选研究的验证证实了新的入学预测框架的稳定性和可靠性.
- 该模型有效地结合了特定时间和位置的变量,从而导致更精确的时间线预测.
结论:
- 这种新的两部分统计方法在临床试验招生预测方面取得了重大进展.
- 该框架为临床试验的战略规划和执行卓越提供了更准确,更可靠的工具.
- 该模型能够整合多样化的数据源,从而提高其对制药企业的实际适用性.
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