用重复的多变量数据的单独或联合模型来估计个体的疾病轨迹,适用于硬质皮质
Ji Soo Kim1,2, Ami A Shah1, Laura K Hummers1
1Division of Rheumatology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America.
PloS one
|April 21, 2025
概括
使用多个生物标志物估计疾病轨迹需要在联合或单独的模型之间做出选择. 联合模型为随机效应提供了更高的效率,特别是在缺少数据的情况下,改善了慢性疾病的临床决策.
科学领域:
- 生物统计学 生物统计学
- 纵向数据分析 纵向数据分析
- 临床生物标志物 临床生物标志物
背景情况:
- 使用临床措施准确估计患者的疾病轨迹在医学中至关重要.
- 多个生物标志物提供了丰富的信息,但也带来了建模挑战:联合分布与单独分布.
- 需要联合模型来充分利用信息,但可能是计算密集的.
研究的目的:
- 导出疾病轨迹的单变量 (分离) 估计与多变量 (组合) 估计的低效率的一般表达式.
- 在双变量和多变量设置中比较联合和分离模型的效率,包括临床案例研究.
- 确定联合建模在哪些条件下比单独建模提供了显著的效率增长.
主要方法:
- 对于分离估计的低效率的一般数学表达式的推导.
- 这些表达式的应用到一个一般的双变形案例.
- 对一项临床案例研究的分析,包括五项纵向测量,包括缺少数据的场景.
主要成果:
- 对人口平均 (固定效应) 轨迹的单独估计几乎与组合估计一样有效.
- 对个体特异性 (随机效应) 轨迹的联合估计可以在一些相关措施缺少数据时显著更有效.
- 联合模型的效率提高源于随机效应的多变量缩小,特别是在缺少数据的情况下.
结论:
- 为了估计人口平均疾病轨迹,对生物标志物的单独建模通常是足够有效的.
- 联合建模为估计个体患者轨迹提供了显著的优势,特别是在处理缺失的生物标志物数据和相关措施时.
- 这些发现支持在慢性疾病管理中使用联合模型,其中生物标志物轨迹为临床决策提供信息.
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