Zhou Zhi-Rui, Wang Wei-Wei, Li Yan, Jin Kai-Rui, Wang Xuan-Yi, Wang Zi-Wei, Chen Yi-Shan, Wang Shao-Jia, Hu Jing, Zhang Hui-Na, Huang Po, Zhao Guo-Zhen, Chen Xing-Xing, Li Bo, Zhang Tian-Song
Department of Radiotherapy, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai 200040, China.
Department of Thoracic Surgery, The Third Affiliated Hospital of Kunming Medical University & Yunnan Provincial Tumor Hospital, Kunming 650118, China.
Ann Transl Med. 2019 Dec;7(23):796. doi: 10.21037/atm.2019.08.63.
This article is the series of methodology of clinical prediction model construction (total 16 sections of this methodology series). The first section mainly introduces the concept, current application status, construction methods and processes, classification of clinical prediction models, and the necessary conditions for conducting such researches and the problems currently faced. The second episode of these series mainly concentrates on the screening method in multivariate regression analysis. The third section mainly introduces the construction method of prediction models based on Logistic regression and Nomogram drawing. The fourth episode mainly concentrates on Cox proportional hazards regression model and Nomogram drawing. The fifth Section of the series mainly introduces the calculation method of C-Statistics in the logistic regression model. The sixth section mainly introduces two common calculation methods for C-Index in Cox regression based on R. The seventh section focuses on the principle and calculation methods of Net Reclassification Index (NRI) using R. The eighth section focuses on the principle and calculation methods of IDI (Integrated Discrimination Index) using R. The ninth section continues to explore the evaluation method of clinical utility after predictive model construction: Decision Curve Analysis. The tenth section is a supplement to the previous section and mainly introduces the Decision Curve Analysis of survival outcome data. The eleventh section mainly discusses the external validation method of Logistic regression model. The twelfth mainly discusses the in-depth evaluation of Cox regression model based on R, including calculating the concordance index of discrimination (C-index) in the validation data set and drawing the calibration curve. The thirteenth section mainly introduces how to deal with the survival data outcome using competitive risk model with R. The fourteenth section mainly introduces how to draw the nomogram of the competitive risk model with R. The fifteenth section of the series mainly discusses the identification of outliers and the interpolation of missing values. The sixteenth section of the series mainly introduced the advanced variable selection methods in linear model, such as Ridge regression and LASSO regression.
本文是临床预测模型构建方法系列文章（该方法系列共16节）。第一节主要介绍临床预测模型的概念、当前应用现状、构建方法与流程、分类，以及开展此类研究的必要条件和当前面临的问题。本系列的第二节主要聚焦多元回归分析中的筛选方法。第三节主要介绍基于逻辑回归和列线图绘制的预测模型构建方法。第四节主要聚焦Cox比例风险回归模型和列线图绘制。本系列的第五节主要介绍逻辑回归模型中C统计量的计算方法。第六节主要介绍基于R语言的Cox回归中C指数的两种常见计算方法。第七节重点介绍使用R语言的净重新分类指数（NRI）的原理和计算方法。第八节重点介绍使用R语言的综合判别指数（IDI）的原理和计算方法。第九节继续探讨预测模型构建后的临床效用评估方法：决策曲线分析。第十节是上一节的补充，主要介绍生存结局数据的决策曲线分析。第十一节主要讨论逻辑回归模型的外部验证方法。第十二节主要讨论基于R语言的Cox回归的深入评估，包括计算验证数据集中的判别一致性指数（C指数）并绘制校准曲线。第十三节主要介绍如何使用R语言的竞争风险模型处理生存数据结局。第十四节主要介绍如何使用R语言绘制竞争风险模型的列线图。本系列的第十五节主要讨论异常值的识别和缺失值的插补。本系列的第十六节主要介绍线性模型中的高级变量选择方法，如岭回归和套索回归。
