一个边缘化的两部分联合模型,用于纵向生物标志物和终端事件,适用于高级头癌
Denis Rustand1,2, Laurent Briollais3, Virginie Rondeau1
1UMR1219, Inserm, Bordeaux Population Health Center, University of Bordeaux, Bordeaux, France.
Pharmaceutical statistics
|September 17, 2023
概括
一个新的统计模型,边缘化的两部分联合模型 (M-TPJM),更好地分析瘤大小变化 (SLD) 和癌症试验中的存活率. 这种方法揭示了治疗方法如何影响整体瘤反应和患者的结果.
科学领域:
- 生物统计学 生物统计学
- 临床试验方法论 临床试验方法论
- 在瘤学瘤学.
背景情况:
- 最长直径的总和 (SLD) 是癌症临床试验中瘤反应的关键纵向生物标志物.
- SLD数据通常是半连续的,有多余的零和右倾,这给分析带来了挑战.
- 现有的有条件的两部分模型分析了SLD和时间到事件数据,但只关注积极生物标志物值的有条件平均值.
研究的目的:
- 提出一个边缘化的两部分联合模型 (M-TPJM),允许共变量影响SLD生物标志物的整体平均值.
- 提供更临床相关的治疗效应对纵向瘤测量和生存结果的分析.
- 通过模拟研究和应用到现实世界的临床试验来评估M-TPJM的性能.
主要方法:
- 开发一个边缘化的两部分联合模型 (M-TPJM) 用于重复的SLD测量和终端事件.
- 模拟研究以评估M-TPJM的估计准确性和覆盖率.
- 将M-TPJM应用于高级头癌的随机临床试验.
主要成果:
- 模拟研究表明,M-TPJM在估计和覆盖方面表现良好.
- 头癌试验的M-TPJM分析表明,与单独的化疗相比,帕尼图穆马布加上化疗改善了完全病变消失的可能性.
- 这表明间接治疗对死亡时间的影响.
结论:
- 边缘化的两部分联合模型 (M-TPJM) 提供了一个统计学上强大的和临床上相关的方法来分析癌症试验中的SLD等纵向生物标志物.
- M-TPJM有效地捕捉了共变量对生物标志物分布的整体影响,提供了比条件模型更深入的见解.
- 头癌试验的结果强调了联合治疗的潜在益处以及M-TPJM在识别这种影响方面的实用性.
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