Kert Viele's blog discusses the FDA's draft guidance on master protocols for drug and biological product development, highlighting key sections on trial design, randomization, control groups, informed consent, and regulatory considerations, while encouraging feedback from experts to enhance the guidance's effectiveness before the comment deadline of February 22.

The blog by Kert Viele discusses the potential biases in clinical trial results, particularly focusing on early stopping trials and the implications of only publishing successful outcomes, emphasizing that while biases exist, their significance varies based on the true response rate and the context of the trial.

Joe Marion's blog discusses the challenges of designing phase I dose-finding studies in oncology using the Continual Reassessment Method (CRM) and Bayesian approaches, emphasizing the importance of selecting appropriate prior distributions to balance patient safety and effective dose escalation, while suggesting that re-parameterizing models can simplify the design process.

On September 28, 2023, Kert Viele will moderate a panel at the ASA Biopharmaceutical Section Regulatory-Industry Statistics Workshop, discussing time trends in ongoing platform trials using real interim analyses from the PRINCIPLE and REMAP-CAP trials, focusing on their impact on clinical trial analyses, modeling adjustments, and the complexities of additive versus interactive time trends.

In his blog, Kert Viele discusses the importance of trial design in Bayesian analysis, emphasizing that while conclusions drawn from completed experiments remain consistent regardless of interim analyses, the design of the trial significantly impacts expected utilities, and optimal designs can enhance trial performance.

The blog by Kert Viele discusses the tradeoffs of using external or synthetic data in clinical trials, highlighting how aggressive use can save patient resources but risks scientific robustness, and emphasizes the importance of understanding the agreement between synthetic and actual trial data to optimize inferential performance while minimizing patient enrollment.