费舍尔信息的分解,以告知样本大小,以开发或更新个人风险的公平和精确的临床预测模型:第1部分:二元结果
Richard D Riley1,2, Gary S Collins3, Rebecca Whittle4,5
1Department of Applied Health Sciences, School of Health Sciences, College of Medicine and Health, University of Birmingham, Birmingham, UK. r.d.riley@bham.ac.uk.
Diagnostic and prognostic research
|July 7, 2025
概括
本研究引入了一种新的方法来确定临床预测模型所需的样本大小,确保准确和公平的个人风险估计. 这种方法有助于研究人员避免过度装配,并提高模型性能.
科学领域:
- 生物统计学 生物统计学
- 临床流行病学临床流行病学
- 医疗信息学 医疗信息学
背景情况:
- 临床预测模型开发中的小样本大小可能导致过度匹配,不稳定和不公平.
- 现有的方法侧重于整体风险估计,但缺乏针对个体级风险准确性和公平性的指导.
研究的目的:
- 提出一种计算样本大小的方法,以确保在临床预测模型中准确和公平的个人级风险估计.
- 为研究人员提供一个实用的框架,在数据收集之前或使用现有数据集时评估样本大小的充分性.
主要方法:
- 分解费舍尔信息矩阵以分析样本大小要求.
- 一个五步过程,涉及人口风险的规范,预测器分布和核心模型 (基于逻辑回归或C统计).
主要成果:
- 导出关闭形式的解决方案,将风险估计差异与信息矩阵,预测值和样本大小联系起来.
- 能够快速计算和检查给定样本大小的个体预测精度.
- 在新的软件模块"pmstabilityss"中实现与现实世界的例子.
结论:
- 拟议的方法有助于研究人员确定适当的样本大小,用于开发或更新预测模型.
- 它特别针对对二元期权结果的准确和公平的个人预测.
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