Bayesian predictive probability for binary outcomes in neurodegenerative diseases.

Abstract

Background: Adaptive clinical trials enable modifications to the study design based on accumulating evidence. The Bayesian predictive probability approach offers a framework for estimating the likelihood of achieving a successful outcome in a future analysis, based on current interim data.

Objective: To estimate the predictive probability of success for binary outcomes in patients with Alzheimer's disease or Ataxia treated with NeuroEPO plus.

Methods: A retrospective Bayesian analysis was conducted using data from exploratory phase II trials as prior information for confirmatory phase III trials in Alzheimer's disease. Predictive probabilities were calculated at interim points with sample sizes of 50, 100, 150, and 176 patients.

Results: The analysis demonstrated that the trial could have been stopped early due to a high probability of success or failures before reaching the full planned sample size.

Conclusions: Bayesian predictive probability is a valuable tool for decision-making in rare diseases, particularly when alternative treatments are limited or ineffective, or when baseline heterogeneity affects outcomes unevenly. This approach enhances interim evaluations by incorporating historical or non-informative priors, allowing for more accurate and efficient trial designs.