基于条件转换模型的多变量参考和容忍区域:适用于血糖标记物
Óscar Lado-Baleato1,2, Carmen Cadarso-Suárez3,4, Thomas Kneib5
1Research Methods Group (RESMET), Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain.
Biometrical journal. Biometrische Zeitschrift
|June 26, 2023
概括
多变量参考区域 (MVR) 通过多项诊断测试来改善健康状况的确定. 新的统计模型 (MCTM) 提高了MVR的解释性和用于临床的共同变量调整.
科学领域:
- 生物统计学 生物统计学
- 医疗决策的制定 医疗决策的制定
- 诊断测试 诊断测试 诊断测试
背景情况:
- 参考间隔对于医疗决策至关重要,但由于可解释性和分布假设,多变量参考区域 (MVR) 的利用不足.
- 对于多变量参考区域 (MVR) 的现有方法通常假设高斯分布,并且缺乏共变量调整,从而限制了它们的临床适用性.
研究的目的:
- 引入使用多变量条件转换模型 (MCTMs) 的多变量参考区域 (MVRs) 的新配方.
- 通过纳入容忍区域来解决MVR的估计不确定性.
- 开发可解释的MVR,易于医生应用,并根据共变量进行调整.
主要方法:
- 基于多变量条件转换模型 (MCTMs) 的MVR新配方的开发.
- 在MVR中包含容忍区域以考虑估计不确定性.
- 在没有参数限制的情况下,对多变量响应变量联合和边际分布的共变量效应的估计.
主要成果:
- 拟议的基于MCTM的有条件MVR证明了与模拟数据的可靠性.
- 该方法成功地应用于涉及两个血糖标记物的真实世界数据集.
- 该方法允许估计对诊断试验结果的潜在非线性协变效应.
结论:
- 新的MCTM框架提供了一种灵活和可解释的方法来构建对共变量调整的MVR.
- 这种统计学进步增强了多变量诊断试验解释的临床实用性.
- 开发的方法为克服复杂临床场景中传统参考间隔的局限性提供了一个有希望的解决方案.
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