In-depth mining of clinical data: the construction of clinical prediction model with R
In-depth mining of clinical data: the construction of clinical prediction model with R
复制标题
临床数据深度挖掘：用R构建临床预测模型
DOI：
10.21037/atm.2019.08.63
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发表时间：
2019-12-01
影响因子：
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通讯作者：
Zhang, Tian-Song
中科院分区：
文献类型：
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作者：
Zhou, Zhi-Rui;Wang, Wei-Wei;Zhang, Tian-Song
本文是构建临床预测模型的方法论系列(本方法学系列共16节)。第一部分主要介绍了临床预测模型的概念、应用现状、构建方法和过程、分类以及开展临床预测模型研究的必要条件和面临的问题。本系列的第二集主要集中在多元回归分析中的筛选方法。第三部分主要介绍了基于Logistic回归和诺模图绘制的预测模型的构建方法。第四集主要介绍了Cox比例风险回归模型和诺模图的绘制。第五部分主要介绍Logistic回归模型中C统计量的计算方法。第六节主要介绍了基于R的COX回归中C指数的两种常用计算方法。第七节重点介绍了基于R的净重分类指数(NRI)的原理和计算方法。第八节主要介绍了使用R的综合判别指数(IDI)的原理和计算方法。第九节继续探讨了预测模型构建后的临床效用的评估方法：决策曲线分析。第十节是对上一节的补充，主要介绍生存结局数据的决策曲线分析。第十一部分主要讨论Logistic回归模型的外部验证方法。第十二章主要讨论了基于R的COX回归模型的深入评价，包括计算验证数据集中的判别一致性指数(C指数)和绘制校准曲线。第十三节主要介绍了如何利用带R的竞争风险模型处理生存数据的结果。第十四节主要介绍了如何绘制带R的竞争风险模型的范数图。本系列的第十六部分主要介绍了线性模型中先进的变量选择方法，如岭回归和套索回归。
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.