通过当前价值联合模型估计移植缺陷,并扩展到替代参数化结构:队列研究
Alev Bakir1, Zeynep Atli2, Eda Kaya3
1Department of Social Pediatrics, Institute of Child Health, Istanbul University, Istanbul, Turkey.
Medicine
|October 21, 2024
概括
联合模型 (JMs) 通过分析纵向估计的膜过率 (eGFR) 以及生存数据来改善移植结果的预测. 当前价值JM在更好的临床决策方面表现优于传统方法.
科学领域:
- 生物统计学 生物统计学
- 医学统计 医学统计
- 临床数据分析 临床数据分析
背景情况:
- 临床实践通常涉及跟踪纵向测量以预测患者的结果.
- 传统的统计方法可能会单独分析纵向数据和生存事件,可能会错过关键的关联.
- 纵向估计的膜过率 (eGFR) 与移植移植移植失败之间的关系需要先进的建模.
研究的目的:
- 为了比较经典方法,扩展的考克斯模型和联合模型 (JMs) 来分析时间变化的纵向eGFR和移植存活率.
- 评估不同的JM参数化,包括当前值和加权累积效应模型.
- 评估JM在移植接受者的动态预测中的实用性.
主要方法:
- 使用了158名脏移植接受者的队列数据集,以及基线和随访数据.
- 使用R统计软件应用扩展的考克斯模型,当前价值JM和加权累积效应JM.
- 通过参数和标准错误的比较,以及合适性标准来评估模型性能.
主要成果:
- 对于每单位日志降低 (eGFR) 的移植失败的危险比为8.80 (扩展Cox),10.58 (当前价值JM) 和3.65 (加权累积效应JM).
- 当前值JM和加权累积效应JM显示了冠状动脉疾病和日志 (eGFR) 之间的显著关联.
- 根据统计标准,当前价值JM的表现优于扩展的考克斯模型和加权累积效应JM.
结论:
- 联合模型,特别是当前值JM,比传统方法更好,用于分析纵向eGFR并预测移植结果.
- 通过考虑eGFR的动态性质,JM提供了更准确的预测,促进了更好的临床决策.
- 模型选择应考虑基线生物标志物水平,趋势,分布和纵向生物标志物的数量.
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