In-depth mining of clinical data: the construction of clinical prediction model with R

Abstract

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 Zhou et al. Clinical prediction models with R

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“…DCA represented net benefit of a clinical decision. To compare the accuracy of our nomogram with that of traditional TNM staging system, the net reclassification index (NRI) and integrated discrimination index (IDI) were analyzed based on the R package of nricens and predictABEL as reported (Zhou et al, 2019).…”

Development of a Ten-lncRNA Signature Prognostic Model for Breast Cancer Survival: A Study with the TCGA Database

“…These sequences were then used for the stepwise multivariate Cox regression analysis, using the R package “survival” (choose a model by AIC in a stepwise algorithm) [ 12 – 14 ]. Based on expression levels and coefficients ( β ) from multivariate Cox proportional hazards regression analysis, a novel ten-lncRNA-based prognostic risk score formula was defined [ 13 – 16 ]. The risk score formula was as follows: …”

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