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在嵌套案例对照研究中评估预测模型的性能时,需要加权指标
Barbara Rentroia-Pacheco1, Domenico Bellomo2, Inge M M Lakeman3,4
1Department of Dermatology, Erasmus Medical Center Cancer Institute, Erasmus University Medical Center, Dr. Molewaterplein 40, Rotterdam, 3015 GD, The Netherlands. b.rentroiapacheco@erasmusmc.nl.
BMC medical research methodology
|May 17, 2024
概括
嵌套案例控制 (NCC) 设计有效地使用昂贵的预测器验证预测模型. 将绩效指标与采样权重进行调整,可确保NCC研究中的模型评估准确,特别是对于罕见的结果.
科学领域:
- 生物统计学 生物统计学
- 流行病学 流行病学
- 医疗信息学 医疗信息学
背景情况:
- 嵌套案例控制 (NCC) 设计为开发和验证具有昂贵或难以获得预测因子的预测模型提供了效率,特别是对于罕见的结果.
- 现有的研究主要针对NCC设计中的预测模型开发,仅限于对强大的模型验证策略的关注.
- 准确的性能评估对于可靠的预测模型至关重要,特别是在像NCC这样的复杂采样方案中.
研究的目的:
- 系统地描述NCC数据中预测模型性能正确评估的基本要素.
- 提出和说明调整绩效指标的方法,以考虑NCC抽样程序.
- 将加权和未加权的绩效指标与完整的队列数据进行比较,以评估偏差.
主要方法:
- 建议通过采样权重调整标准绩效指标 (C指数,基于值的指标,观察到预期事件的比率 (O/E比率),校准斜率,决策曲线分析).
- 通过使用基于人口的鹿特丹研究数据验证了乳腺和卵巢疾病发病率分析和载体估计算法 (BOADICEA版本 5).
- 通过加权和未加权的方法,将来自全队列数据的绩效指标与来自NCC数据集 (匹配和不匹配) 的绩效指标进行了比较.
主要成果:
- 在NCC数据集中的未加权性能指标存在偏差;例如,未加权C指数 (0.61) 与全队列C指数 (0.65) 不同.
- 在NCC数据集中,加权C指数 (0.65) 和O/E比率 (1.68) 与全队列值 (分别为0.65和1.69) 密切匹配,证明了不偏见的估计.
- 权重调整纠正了基于值的指标和决策曲线分析中的偏差,在匹配的NCC设计中偏差增加,但仍可通过权重进行补偿.
结论:
- 嵌套病例控制研究对于评估具有复杂预测因子和罕见结果的预测模型非常有价值.
- 在NCC研究中的性能指标需要使用采样权重进行调整,以准确反映模型性能.
- 重量调整确保了NCC设计中的预测模型的可靠和公正的评估,这对于临床实用性至关重要.
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