Sample size calculation for recurrent event data with additive rates models

Area of Science:

• Biostatistics
• Clinical Trial Design
• Epidemiology

Background:

• Recurrent events are common primary endpoints in clinical trials.
• Existing sample size calculation methods often assume multiplicative treatment effects.
• Additive treatment effects offer more intuitive clinical interpretation.

Purpose of the Study:

• To develop and investigate new methods for sample size calculation in recurrent event trials.
• To focus on additive treatment effect models for superiority, non-inferiority, and equivalence trials.
• To provide flexible methods accounting for various trial complexities.

Main Methods:

• Development of novel sample size calculation methodologies based on the additive rates model.
• Incorporation of flexible baseline rate functions, staggered entry, and random dropout.
• Consideration of overdispersion in event counts.

Main Results:

• Proposed methods perform well across diverse simulation settings.
• The additive rates model provides a clinically meaningful alternative to multiplicative models.
• The methods are applicable to superiority, non-inferiority, and equivalence trial designs.

Conclusions:

• The presented methods offer a robust and practical approach to sample size calculation for recurrent event trials with additive treatment effects.
• These methods enhance the design flexibility and interpretability of clinical trials.
• Real-world data application demonstrates the utility of the proposed techniques.