An R-Based Landscape Validation of a Competing Risk Model

Summary

The present protocol describes codes in R for evaluating the discrimination and calibration abilities of a competing risk model, as well as codes for the internal and external validation of it.

Abstract

The Cox proportional hazard model is widely applied for survival analyses in clinical settings, but it is not able to cope with multiple survival outcomes. Different from the traditional Cox proportional hazard model, competing risk models consider the presence of competing events and their combination with a nomogram, a graphical calculating device, which is a useful tool for clinicians to conduct a precise prognostic prediction. In this study, we report a method for establishing the competing risk nomogram, that is, the evaluation of its discrimination (i.e., concordance index and area under the curve) and calibration (i.e., calibration curves) abilities, as well as the net benefit (i.e., decision curve analysis). In addition, internal validation using bootstrap resamples of the original dataset and external validation using an external dataset of the established competing risk nomogram were also performed to demonstrate its extrapolation ability. The competing risk nomogram should serve as a useful tool for clinicians to predict prognosis with the consideration of competing risks.

Introduction

In recent years, emerging prognostic factors have been identified with the development of precision medicine, and prognostic models combining molecular and clinicopathological factors are drawing increasing attention in clinical settings. However, non-graphical models, such as the Cox proportional hazard model, with results of coefficient values, are difficult for clinicians to understand¹. In comparison, a nomogram is a visualization tool of regression models (including the Cox regression model, competing risk model, etc.), a two-dimensional diagram designed for the approximate graphical computation of a mathematical function^....

Protocol

The Surveillance, Epidemiology, and End Results (SEER) database is an open-access cancer database that only contains deidentified patient data (SEER ID: 12296-Nov2018). Therefore, this study was exempted from the approval of the review board of the Affiliated Jinhua Hospital, Zhejiang University School of Medicine.

1. Data preparation and R packages preparation

1. Prepare and import the data.
   > Dataset <- read.csv("…/Breast cancer Data.xlsx") #Import data.
   NOTE: The data are uploaded in Supplementary File 1.
2. Install and load the R packages.

Results

In this study, data of patients with breast cancer were retrieved from the SEER database and served as example data. The SEER database provides data on cancer representing around 34.6% of the United States population, and permission to access the database was obtained (reference number 12296-Nov2018).

Two nomograms (Figure 1), both including histological type, differentiated grade, T stage, and N stage, were established using the direct method and the weighted met.......

Discussion

This study compared competing risk nomograms established by two distinct methods and conducted evaluation and validation of the established nomograms. Specifically, this study provided a step-by-step tutorial for establishing the nomogram based on a direct method, as well as calculating the C-index and plotting the calibration curves.

The rms package in R software is widely used for the construction and evaluation of Cox proportional hazard models, but it is not applicable for competi.......

Materials

Name	Company	Catalog Number	Comments
R software	None	Not Applicable	Version 3.6.2 or higher
Computer system	Microsoft	Windows 10	Windows 10 or higher