基于总结数据的决策曲线分析
Iztok Hozo1, Gordon Guyatt2, Benjamin Djulbegovic3
1Department of Mathematics, Indiana University Northwest, Gary, Indiana, USA.
Journal of evaluation in clinical practice
|December 4, 2023
概括
现在可以使用汇总数据进行决策曲线分析 (DCA),消除对个体患者数据 (IPD) 的需求. 这一进步有助于更广泛地整合精准医学和个性化决策的预测模型.
科学领域:
- 生物统计学 生物统计学
- 临床决策 临床决策
- 精准医学是一门精准的医学.
背景情况:
- 精准医学需要将预测模型与决策曲线分析 (DCA) 等决策分析框架相结合.
- 目前的DCA应用程序需要个体患者数据 (IPD),通常无法访问.
- 开发用于DCA总数据的方法可以增强精准医学的采用.
研究的目的:
- 提出一个统计框架,使得DCA仅使用从预测模型概率的总数据 (平均值和标准偏差) 来实现.
- 通过模拟和现实世界数据集,通过对比传统的IPD-based DCA来验证这种聚合数据方法.
主要方法:
- 用预测模型概率的平均值和标准偏差开发了DCA总量数据的统计框架.
- 进行了广泛的模拟,以比较基于聚合物的DCA与基于IPD的DCA.
- 将框架应用于使用IPD的四种不同的预测模型,这些模型来自对他类药物,临终关怀转诊,血栓预防和鼻腔阻塞综合征预防的研究.
主要成果:
- 当预测模型被精确校准时,模拟显示了总和IPD DCA之间的微不足道差异.
- 对于充足的动力模型,DCA的总数据与IPD衍生的DCA结果非常接近.
- 由于固有的不稳定性,在样本规模较小的模型中,观察到总和基于IPD的DCA之间存在较大的差异.
结论:
- 从充足的动力和校准模型中使用总结统计数据 (平均值和SD) 的DCA与基于IPD的DCA非常接近.
- 使用聚合数据显著扩大了DCA的适用性,克服了IPD可访问性限制.
- 这种方法促进了预测和决策建模的整合,以推进个性化患者护理.
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