临床预测模型开发与验证的样本量估算

doi:10.3969/j.issn.1673-5765.2024.05.002

中国卒中杂志 ›› 2024, Vol. 19 ›› Issue (5): 489-495.DOI: 10.3969/j.issn.1673-5765.2024.05.002

临床预测模型开发与验证的样本量估算

姜英玉，谷鸿秋

北京 100070 首都医科大学附属北京天坛医院国家神经系统疾病临床医学研究中心

收稿日期:2024-04-15 出版日期:2024-05-20 发布日期:2024-05-20
通讯作者: 谷鸿秋 guhongqiu@yeah.net
基金资助:
国家自然科学基金项目（72004146）
北京市医院管理中心“青苗”人才计划（QML20210501）
北京市医院管理中心“培育”人才计划（PX2021024）

Sample Size Estimation for the Development and Validation of Clinical Prediction Models

JIANG Yingyu, GU Hongqiu

China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China

Received:2024-04-15 Online:2024-05-20 Published:2024-05-20
Contact: GU Hongqiu, E-mail: guhongqiu@yeah.net

摘要/Abstract

摘要： 临床预测模型在医学领域的应用受到越来越多的关注和重视。合理且充足的样本量对预测模型研究的可行性和质量保障具有重要意义。基于此，本文总结了临床预测模型开发和验证中的样本量估算策略，提出了在样本量估算时需重点关注的问题，同时探讨了样本量不足的问题，以期能够帮助研究者合理地估算预测模型所需的样本量，从而提高临床预测模型研究的质量。

关键词: 临床预测模型; 样本量; 模型开发; 模型验证

Abstract: The application of clinical prediction models in the medical field is receiving increasing attention. Reasonable and sufficient sample size is of great importance to the feasibility and quality assurance of prediction model research. Based on this, this paper summarized the strategies for sample size estimation in the development and validation of clinical prediction models, outlined the key issues that should be paid attention to in sample size estimation, and also discussed the problems related to insufficient sample size, to help researchers estimate the sample size reasonably and improve the quality of clinical prediction model research.

Key words: Clinical prediction model; Sample size; Model development; Model validation

中图分类号:

姜英玉, 谷鸿秋. 临床预测模型开发与验证的样本量估算[J]. 中国卒中杂志, 2024, 19(5): 489-495.

JIANG Yingyu, GU Hongqiu. Sample Size Estimation for the Development and Validation of Clinical Prediction Models[J]. Chinese Journal of Stroke, 2024, 19(5): 489-495.

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临床预测模型开发与验证的样本量估算

Sample Size Estimation for the Development and Validation of Clinical Prediction Models

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