---
title: Competing Risks in Survival Analysis
description: What is a Competing Event? In survival analysis, a competing event is an event that prevents the occurrence of the primary event of interest. For example…
---
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# Competing Risks in Survival Analysis
# What is a Competing Event?
In survival analysis, a competing event is an event that prevents the occurrence of the primary event of interest. For example, if you're studying time to cancer relapse, death from another cause would be a competing event, as it precludes the possibility of relapse.
Standard Kaplan-Meier curves assume that no other event can interfere with the event of interest. This can lead to overestimated event probabilities in real-world scenarios. Using competing risks provides a more realistic view of patient outcomes.
Selecting competing events allows to use a cumulative incidence function (CIF), which provides more accurate probability estimates when multiple types of events can occur.
# How to Use This Option?
You can enable competing risks in just a few steps:
1. Run a survival analysis on a primary event variable (e.g., "Cancer relapse").
2. Open the “Analysis options and censoring” panel in the configuration area.
3. Select one or more event-type variables under the “Competing events” section.
4. The analysis will automatically refresh and display the new results using the competing risks method.
## Important Notes
This option is only available for:
- Univariate survival analyses
- Bivariate survival analyses (with a grouping variable)
**It is not compatible with multivariate models such as the Cox proportional hazards model (for now).**
# Methodology
When one or more competing events are defined:
- The analysis uses the Aalen-Johansen method to estimate cumulative incidence.
- If a comparing variable is selected, Gray’s test is used to compare incidence curves across groups.
# Why the Graphs Look Different?
When competing events are selected:
- The analysis uses the Aalen-Johansen estimator instead of the Kaplan-Meier method.
- **The resulting graph shows Cumulative Incidence Functions (CIFs), which represent the actual probability of the event occurring over time, accounting for the risk of other competing events.**
- This contrasts with Kaplan-Meier curves, which assume that censored cases could still experience the event later, even if they've had a competing event, which leads to overestimated probabilities.
In CIF plots:
- The curve typically rises more slowly than in KM plots.
- The y-axis represents the cumulative probability of experiencing the event of interest in the presence of competing events.
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