Introduction to Dealing with Non-proportional Hazards

Key points:

• Non-Proportional Hazards: Understanding when and why the proportional hazards assumption fails.
• Hazard Functions: Differences between survival functions and hazard functions; interpreting dynamics over time.
• Data Visualization: Importance of visualizing hazard functions alongside Kaplan-Meier curves.
• Clinical Context: Collaborating with clinicians to understand treatment effects and disease dynamics.
• Effect Quantification: Exploring alternatives to hazard ratios when proportionality doesn’t hold.
• Trial Design: Challenges in designing studies with non-proportional hazards and strategies to address them.
• Simplification Risks: Avoiding oversimplifications like responder analysis or arbitrary sample size increases.
• Stakeholder Communication: Explaining complex survival data effectively to non-statisticians.
• Regulatory Considerations: Balancing valid hypothesis testing with meaningful effect quantification.
• Actionable Insights: Practical steps for statisticians to improve survival analysis and trial design.

Dealing with non-proportional hazards is a complex but critical aspect of survival analysis, and understanding it can make a significant difference in your work. In this episode, Kaspar and I covered everything from hazard functions and survival curves to practical strategies for trial design and effect quantification. If you found these insights valuable, don’t keep them to yourself!

Share this episode with your friends and colleagues who work with survival analysis or clinical trials. And if you haven’t already, make sure to subscribe so you never miss an episode of The Effective Statistician. Let’s work together to elevate the impact of statistics in healthcare!