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Comparing the Survival Analysis of Two or More Groups

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...

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Kaplan-Meier Approach

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Introduction To Survival Analysis

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Updated: May 8, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

When do we need competing risks methods for survival analysis in nephrology?

Marlies Noordzij¹, Karen Leffondré, Karlijn J van Stralen

¹ERA-EDTA Registry, Department of Medical Informatics, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands.

Nephrology, Dialysis, Transplantation : Official Publication of the European Dialysis and Transplant Association - European Renal Association

|August 27, 2013

Summary

Competing risks in survival analysis can distort results. Specialized methods are crucial for accurate analysis of events like death on dialysis, especially when kidney transplants compete. Ignoring these risks leads to inappropriate conclusions.

Keywords:

censoring competing risks epidemiology statistics survival analysis

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Area of Science:

Nephrology
Biostatistics
Medical Research Methodology

Background:

Survival analysis is frequently used to study events such as death.
Competing risks, events that hinder or alter the probability of the event of interest, pose a significant challenge.
Examples include kidney transplantation competing with death in dialysis patients.

Purpose of the Study:

To highlight the problem of competing risks in survival analysis within nephrology research.
To explain how different analytical techniques can impact study outcomes when competing risks are present.
To advocate for the use of specialized methods for competing risks data.

Main Methods:

Discussion of conventional survival analysis methods (Kaplan-Meier, Cox regression) and their limitations with competing risks.
Explanation of the concept of competing risks and their impact on event probability.
Introduction to alternative methods designed for competing risks data analysis.

Main Results:

Conventional survival analysis methods may yield inappropriate results when competing risks are ignored.
The choice of analytical method significantly influences the interpretation of survival data in the presence of competing events.
Specialized methods are necessary for accurate assessment of event occurrences.

Conclusions:

Competing risks are a critical consideration in survival analyses, particularly in nephrology.
Standard survival analysis techniques are often unsuitable for data with competing risks.
Adoption of appropriate competing risks methodologies is essential for reliable research findings in nephrology.