A Phase I Bayesian Adaptive Design to Simultaneously Optimize Dose and Schedule Assignments Both Between and Within...

Area of Science:

• Clinical Trials
• Pharmacology
• Biostatistics

Background:

• Traditional Phase I trial designs often assign a fixed dose-schedule, even if toxic.
• Lack of systematic methods for intra-patient dose-schedule optimization hinders clinical practice.

Purpose of the Study:

• To propose an adaptive Phase I clinical trial design for optimizing intra-patient dose-schedule assignments.
• To improve patient outcomes by dynamically adjusting treatment within individual participants.

Main Methods:

• Developed a Bayesian non-mixture cure rate model for adaptive dose-schedule assignment.
• Incorporated per-administration dose as a covariate within the model.
• Simulated trial scenarios to evaluate the proposed design.

Main Results:

• The proposed design effectively identifies safe dose and schedule combinations.
• Achieved comparable safety identification to traditional methods.
• Assigned a greater number of patients to safe and effective dose-schedule combinations.

Conclusions:

• The adaptive intra-patient dose-schedule design offers a more realistic and patient-centered approach to Phase I trials.
• This method enhances the efficiency of identifying optimal treatment regimens.
• Supports improved patient outcomes in early-phase drug development.