bayesian-reanalysis

Bayesian analysis calculates the probability of a treatment effect by integrating literature-derived priors with clinical trial data through Monte Carlo simulations, yielding posterior distributions that quantify the likelihood of a clinically meaningful benefit.

Prior sensitivity analysis assesses the robustness of Bayesian clinical trial conclusions by evaluating posterior outcomes across skeptical, evidence-based, and enthusiastic prior specifications. It ensures observed treatment effect probabilities are not artifacts of an arbitrarily chosen prior.

Bayesian re-analysis of underpowered trials combines existing literature-derived prior evidence with observed trial data via Monte Carlo sampling. This approach quantifies the probability of a meaningful treatment effect that traditional frequentist hypothesis testing might fail to detect.

Bayesian Monte Carlo re-analysis supports various clinical trial endpoint types including binary, continuous, and survival data. It calculates posterior distributions, Highest Density Intervals (HDI), and Region of Practical Equivalence (ROPE) metrics for each supported format.

Highest Density Interval (HDI) identifies the narrowest range containing the most credible posterior parameter values, while Region of Practical Equivalence (ROPE) defines a range of effect sizes considered practically meaningless. Comparing them determines if a treatment effect is statistically and practically significant.

Bayesian re-analysis may not be appropriate when no prior literature exists to elicit evidence-based priors, or when strictly frequentist regulatory submissions are required. It requires subjective prior specification, which can influence posterior conclusions if sensitivity analysis is insufficient.