Design and analysis features used in small population and rare disease trials: A targeted review

Objectives: Frequentist trials in Rare disease/small population trials often require unfeasibly large sample size to detect minimum clinically important differences. A targeted review was performed investigating what design and analysis methods these trials use when facing restricted recruitment. Study Design and Setting: Targeted Review searching EMBASE and MEDLINE for Phase II-IV RCTs reporting 'rare' disease or 'small population' within title or abstract, since 2009. Results: A total of 6,128 articles were screened with 64 trials eligible (four Bayesian, 60 frequentist trials). Frequentists trials had planned power ranging 72-90% (median: 80%) but reported recruiting a mean of 6.6% below the planned sample size (n = 38) [median 0%, IQR (-5%, 5%)], most used standard type I error (52 used 5% and one used 1%), and the average standardized effect was high (0.7) with 50% missing their assumed level. Of the four Bayesian trials, three used informed priors, two and one trials performed sensitivity analysis for the impact of priors on design and analysis respectively. Historical data, expert consensus, or both were used to construct informative priors. Bayesian trials required 30-2400% less participants than using frequentist frameworks. Conclusion: Bayesian trials required lower sample size through use of informative priors. Most frequentists didn't achieve their target sample size. Bayesian methods offer promising solutions for such trials but are underutilized. (C) 2022 Elsevier Inc. All rights reserved.

Automated summary

Frequentist trials in rare disease/small population trials often require unfeasibly large sample size, while Bayesian trials can reduce sample size by using informative priors. Most frequentist trials did not reach the target sample size. Bayesian methods offer promising solutions for such trials but are underutilized.