Competing Risks Analysis Made Simple

Picture this: You're analyzing clinical trial data where patients can experience multiple types of events—recovery, relapse, or death. Traditional survival analysis falls short because it can't handle these competing risks where one outcome prevents others from occurring. That's where competing risks analysis becomes your statistical superhero.

With Sourcetable's AI-powered platform, you can perform sophisticated competing risks modeling without wrestling with complex statistical software. Our intuitive interface makes it simple to analyze survival data with multiple endpoints, calculate cumulative incidence functions, and interpret results—all while maintaining the flexibility of a spreadsheet.

Understanding Competing Risks Analysis

Competing risks analysis is a statistical method used when subjects can experience one of several mutually exclusive events. Think of it as analyzing a race where multiple finish lines exist, but crossing one prevents you from reaching the others.

Unlike standard survival analysis that focuses on time to a single event, competing risks analysis considers:

• Multiple event types: Different outcomes that can occur
• Mutual exclusivity: Experiencing one event prevents others
• Censoring: Subjects who don't experience any event during follow-up
• Time-to-event: When each type of event occurs

This approach provides more nuanced insights than traditional methods, especially in medical research, reliability engineering, and risk assessment where multiple competing outcomes are common.

Why Choose Sourcetable for Competing Risks Analysis

Common Applications of Competing Risks Analysis

Competing Risks Analysis in Sourcetable

Competing Risks Analysis Examples

Example 1: Clinical Trial Analysis

A pharmaceutical company is analyzing a cancer treatment trial where patients can experience three competing outcomes: disease progression, death from treatment toxicity, or death from other causes. The traditional approach of analyzing each endpoint separately would overestimate the probability of any single event.

Using Sourcetable's competing risks analysis:

• Data setup: Import patient data with time-to-event, event type, and baseline characteristics
• Model specification: 'Analyze competing risks of progression, toxicity death, and other death with age and treatment as predictors'
• Key findings: Treatment reduces progression risk (HR=0.65, p=0.02) but increases toxicity risk (HR=1.8, p=0.04)
• Clinical insight: Cumulative incidence plots show net benefit of treatment despite increased toxicity

Example 2: Equipment Reliability Study

A manufacturing company needs to analyze pump failures in their facility. Pumps can fail due to mechanical wear, electrical issues, or external damage. Understanding these competing failure modes helps optimize maintenance strategies.

The analysis reveals:

• Mechanical wear: Dominant failure mode after 18 months of operation
• Electrical issues: More common in pumps with high usage patterns
• External damage: Random events requiring different preventive measures
• Maintenance optimization: Develop targeted interventions for each failure mode

Example 3: Customer Churn Analysis

A subscription service wants to understand why customers cancel. Customers may leave due to pricing concerns, service quality issues, or competitor offerings. Each reason requires different retention strategies.

The competing risks analysis shows:

• Price sensitivity: Highest risk factor for budget-conscious segments
• Service quality: Major driver for premium customers
• Competition: Affects all segments but varies by product features
• Retention strategy: Segment-specific interventions based on dominant risk patterns

Statistical Methods and Implementation

Sourcetable implements industry-standard competing risks methods with modern computational efficiency:

Fine-Gray Subdistribution Hazard Model

The subdistribution hazard model directly models the cumulative incidence function for each competing risk. This approach is particularly useful when you want to understand the absolute risk of experiencing a specific event type in the presence of competing risks.

Key features:

• Models cumulative incidence directly
• Handles competing risks naturally
• Provides intuitive hazard ratio interpretations
• Suitable for prediction and risk assessment

Cause-Specific Hazard Models

Cause-specific models analyze the instantaneous risk of each event type among subjects still at risk. This approach is valuable for understanding the biological or mechanistic processes underlying each competing risk.

Benefits include:

• Clear biological interpretation
• Familiar Cox regression framework
• Separate models for each competing risk
• Useful for mechanistic understanding

Model Validation and Diagnostics

Sourcetable automatically performs comprehensive model validation:

• Proportional hazards testing: Schoenfeld residuals and time-interaction tests
• Model fit assessment: Concordance statistics and calibration plots
• Assumption checking: Linearity, proportionality, and independence tests
• Cross-validation: Out-of-sample prediction performance

Ready to master competing risks analysis?