Purpose: In rare diseases, small sample sizes and high intrapopulation variability often produce insufficient statistical power, necessitating alternative analytical approaches that can efficiently interpret limited data. Methods: A Bayesian framework, which combined both prior knowledge from a Phase 2 trial and observed data from a Phase 3 trial prematurely terminated for futility, was developed to assess the likelihood of achieving clinically meaningful treatment effects with ARRY-371797. Results: The Bayesian reanalysis yielded a posterior Hazard Ratio (HR) of 0.54, indicating a possible 46% reduction in the risk of worsening heart failure or all-cause mortality compared to control. The probability that ARRY-371797 has any efficacy (HR < 1) was 93.75%. The interpretation of treatment efficacy was modestly impacted by the HR thresholds, with 89.89% probability of achieving at least a 10% reduction in the HR (moderate efficacy) and 83.82% probability of achieving a 20% reduction (high efficacy). In the sensitivity analyses, the probability of any efficacy was between 75.68% (non-informative prior) and 85.48% (optimistic prior). Conclusions: By integrating prior knowledge, the Bayesian reanalysis overcame some of the limitations of a frequentist approach and uncovered potential efficacy signals that were previously obscured. A Bayesian framework should be considered more frequently in rare disease research, where traditional statistical methods often fail to fully capture the potential efficacy of novel treatments.