在第三阶段确认性试验中,对缺失数据的多重推算的使用的监管经验
Mouna Sassi-Sayadi1, Pierre Verweij2, Peter Cornelisse2
1Idorsia Pharmaceuticals Ltd, Hegenheimermattweg 91, Allschwil, 4123, Switzerland. Mouna.Sassi-Sayadi@viatris.com.
Therapeutic innovation & regulatory science
|October 10, 2025
概括
本研究应用多重推算 (MI) 来处理高血压试验中缺少的数据,解决重复测量混合模型 (MMRM) 的局限性. 监管机构现在期望在初级试验结果中缺失非随机 (MNAR) 分析.
科学领域:
- 生物统计学 生物统计学
- 临床试验 临床试验
- 药物经济学 药物经济学
背景情况:
- 重复测量混合模型 (MMRM) 是标准的,但面临着随机失踪 (MAR) 假设和排除没有基线后数据的患者的审查.
- 这种排除与治疗意图 (ITT) 原则相冲突,这是临床试验分析的基石.
- 监管机构正在改变他们对临床试验提交文件中缺少数据处理的期望.
研究的目的:
- 证明多重推算 (MI) 在高血压临床试验中解决缺失数据的应用.
- 讨论有关转向基于假设的MNAR分析的监管相互作用.
- 突出在初级临床试验分析中对基于MNAR的方法的期望日益增加.
主要方法:
- 应用多重推算 (MI) 技术来处理高血压临床试验数据集中的缺失数据点.
- 对MI结果与传统的MMRM方法进行比较分析.
- 与监管机构关于分析方法的互动的文件.
主要成果:
- 多重推算 (MI) 提供了一个可行的替代方案来解决缺失的数据,更好地与ITT原则保持一致.
- 监管机构越来越多地要求并接受基于MNAR的分析作为初级试验结果的一部分.
- 该研究说明了实施和讨论这些先进的统计方法的实际例子.
结论:
- 多重推算 (MI) 提供了一种强大的方法来管理临床试验中缺少的数据,特别是当MAR假设存在疑问时.
- 将基于MNAR的分析纳入初级评估的趋势意味着对临床试验数据的监管期望发生了重大转变.
- 有效的沟通和适应不断变化的监管要求对于成功提交临床试验申请至关重要.
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