Hazards Constitute Key Quantities for Analyzing, Interpreting and Understanding Time-to-Event Data.

Area of Science:

• Biostatistics
• Survival Analysis
• Causal Inference

Background:

• Censored time-to-event data necessitates unique statistical approaches.
• Hazards are key in survival analysis due to their conditional nature and use of risk sets.
• The conditional nature of hazards poses challenges for causal interpretation in randomized trials.

Purpose of the Study:

• To address the critique of hazard-based causal inference in survival analysis.
• To demonstrate how a functional viewpoint enables causal interpretation of hazards.
• To illustrate the application of hazard analysis in benefit-risk assessment.

Main Methods:

• Surveying the dilemma of hazard interpretation in randomized trials.
• Applying a functional perspective to enable causal interpretations.
• Utilizing examples from benefit-risk assessment to parameterize situations with hazards.

Main Results:

• Prolonged survival from a treatment may coincide with more adverse events, without necessarily indicating a worse safety profile.
• Hazards can effectively parameterize complex benefit-risk scenarios.
• Censoring does not automatically necessitate a "what if no censoring?" analysis.

Conclusions:

• Causal interpretation of hazard contrasts is achievable through a functional approach.
• Analyses based on hazards should be routinely translated into probabilities for clearer causal inference.
• Understanding survival analysis techniques is crucial, especially when dealing with censoring and causal questions.