多个非线性纵向和竞争风险的贝叶斯联合模型,用于多发性骨髓瘤的动态预测结果:联合估计和纠正的两阶段方法
Danilo Alvares1, Jessica K Barrett1, François Mercier2
1MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
Statistics in medicine
|January 27, 2025
概括
这项研究引入了贝叶斯联合模型,通过分析M蛋白和治疗过渡等纵向生物标志物来预测多发性骨髓瘤 (MM) 的临床事件. 该模型有助于对患者结果进行动态预测.
科学领域:
- 在瘤学瘤学.
- 生物统计学 生物统计学
- 医疗信息学 医疗信息学
背景情况:
- 预测多发性骨髓瘤 (MM) 的临床事件是复杂的,因为疾病的进展受到M蛋白等生物标志物的影响.
- 了解纵向生物标志物模式对治疗过渡和生存的影响对于患者管理至关重要.
研究的目的:
- 开发贝叶斯联合模型来分析多个纵向生物标志物和MM患者死亡和治疗过渡的竞争风险.
- 评估拟议的联合模型的同时和顺序估计方法.
- 用现实世界的数据来验证模型,以便对临床事件进行动态预测.
主要方法:
- 提出了贝叶斯联合模型,将纵向生物标志物 (例如M蛋白) 与竞争风险 (死亡,下一线治疗) 整合起来.
- 探索了两个估计策略:同时和纠正的两阶段顺序方法.
- 将模型应用于追溯的美国MM患者队列 (2015-2022),使用培训和测试数据集进行验证.
主要成果:
- 该研究成功开发和验证了MM的联合建模框架.
- 评估了同时和顺序估计方法的有效性和计算效率.
- 该模型展示了基于纵向数据的临床事件动态预测的能力.
结论:
- 拟议的贝叶斯联合模型有效地整合了纵向生物标志物和竞争风险,以改善MM的临床事件预测.
- 评估的估计方法为模型实施提供了灵活的方法.
- 该框架支持个性化治疗策略和MM患者的结果预测.
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