统计基础:为开发和验证临床预测模型的样本大小考虑
Glen P Martin1, Richard D Riley2,3, Joie Ensor2,3
1Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom.
概括
确保适当的样本大小对于开发可靠的临床预测模型至关重要. 本书概述了样本大小计算方法,所需数据和可用于准确模型开发和验证的软件.
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 (CPM) 整合个人数据以估计健康风险.
- 不充分的样本大小可能导致不稳定的模型和不准确的性能估计,可能会损害临床实践.
- 最近的进展包括在CPM开发和验证中用于最低样本大小估计的特定公式.
研究的目的:
- 为CPM提供关于样本大小计算标准的统计基础.
- 详细说明这些计算所需的信息.
- 用实例说明样本大小方法的实施,并审查可用的软件.
主要方法:
- 最近开发的样本大小公式的概述,用于CPM开发和外部验证.
- 对样本大小计算的数据要求的描述.
- 插图工作示例和软件审查,用于实施样本大小标准.
主要成果:
- 该入门书提供了对CPM的基本样本大小计算方法的全面概述.
- 它详细说明了准确执行这些计算所需的输入.
- 经过工作的示例和软件审查有助于实际应用和实施.
结论:
- 适当的样本大小对于CPM的稳定性和精确性能估计至关重要.
- 本书为研究人员提供了必要的知识和工具,以确定适合模型开发和验证的样本大小.
- 利用所介绍的方法和软件可以提高预测模型的临床效用和安全性.
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