Cancer immunotherapy trial design with random delayed treatment effect and cure rate

Area of Science:

• Oncology
• Biostatistics
• Clinical Trial Design

Background:

• Cancer immunotherapies show promise for advanced-stage cancers.
• Immunotherapy trials present unique challenges, including delayed treatment effects and long-term survivors, leading to nonproportional hazards.
• Existing trial designs struggle to adequately account for these complexities.

Purpose of the Study:

• To propose a general random delayed cure rate model for designing cancer immunotherapy trials.
• To develop a sample size formula for a weighted log-rank test within this new model.
• To evaluate the accuracy and sensitivity of the proposed methods.

Main Methods:

• Development of a general random delayed cure rate model.
• Derivation of a sample size formula for a weighted log-rank test.
• Simulation studies to assess sample size estimation accuracy and compare with existing methods.
• Investigation of the sensitivity to random delay time misspecification.

Main Results:

• The proposed random delayed cure rate model provides a framework for designing immunotherapy trials.
• A derived sample size formula offers improved estimation accuracy.
• Simulation results demonstrate the model's performance and highlight sensitivities to delay time assumptions.

Conclusions:

• The developed random delayed cure rate model and sample size formula are valuable tools for designing robust cancer immunotherapy trials.
• Accurate sample size estimation is crucial for trials with delayed effects and long-term survivors.
• The study provides insights into managing complexities in immunotherapy trial design.