Biostatistics

For single arm trials, a treatment is evaluated by comparing the results to historically reported summary statistics. Such a historically controlled trial is often analyzed as if summary statistics from previous trials were known without variation. We develop a test of treatment efficacy and sample size calculation for historically controlled trials that considers this variation. Using a Bayesian approach we take into account both variation due to estimation and variation in the estimand from trial to trial. As an example, we apply these methods to a clinical trial for amyotrophic lateral sclerosis (ALS) using data from the placebo groups of sixteen trials. We find that when attempting to detect a small effect size, historically controlled trials would require more patients than concurrently controlled trials. However, when the treatment effect size is large, historically controlled trials can be an efficient approach to demonstrating treatment benefit. We also show that utilizing patient level data for the prognostic covariates can reduce the sample size required for a historically controlled trial.