Establishing a Competing Risk Regression Nomogram Model for Survival Data

Summary

Presented here is a protocol to build nomograms based on the Cox proportional hazards regression model and competing risk regression model. The competing method is a more rational method to apply when competing events are present in the survival analysis.

Abstract

The Kaplan–Meier method and Cox proportional hazards regression model are the most common analyses in the survival framework. These are relatively easy to apply and interpret and can be depicted visually. However, when competing events (e.g., cardiovascular and cerebrovascular accidents, treatment-related deaths, traffic accidents) are present, the standard survival methods should be applied with caution, and real-world data cannot be correctly interpreted. It may be desirable to distinguish different kinds of events that may lead to the failure and treat them differently in the analysis. Here, the methods focus on using the competing regression model to identify significant prognostic factors or risk factors when competing events are present. Additionally, nomograms based on a proportional hazard regression model and a competing regression model are established to help clinicians make individual assessments and risk stratifications in order to explain the impact of controversial factors on prognosis.

Introduction

The time to event survival analysis is quite common in clinical studies. Survival data measure the time span from the start time until the occurrence of the event of interest, but the occurrence of the event of interest is often precluded by another event. If more than one type of end point is present, they are called competing risks end points. In this case, the standard hazard analysis (i.e., Cox proportional cause-specific hazards model) often does not work well because individuals experiencing another type of event are censored. Individuals who experience a competing event often remain in the risk set, as the competing risks are usually not independent. Therefore,....

Protocol

The research protocol was approved by the Ethics Committee of Jinhua Hospital, Zhejiang University School of Medicine. For this experiment, the cases were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. SEER is an open-access database that includes demographic, incidence and survival data from 18 population-based cancer registries. We registered on the SEER website and signed a letter of assurance to acquire the research data (12296-Nov2018).

1. Data source

Discussion

The overall goal of the current study was to establish a specific competing-risk nomogram that could describe real-world diseases and to develop a convenient individual assessment model for clinicians to approach treatment decisions. Here, we provide a step-by-step tutorial for establishing nomograms based on the Cox regression model and competing risk regression model and further performing subgroup analysis. Zhang et al.¹⁸ introduced an approach to create a competing-risk nomogram, but the main .......

Materials

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