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Design and analysis features used in small population and rare disease trials: A targeted review
Giles Partington1, Suzie Cro1, Alexina Mason2
1Imperial Clinical Trials Unit, Imperial College London, 1st Floor Stadium House, 68 Wood Lane, London W12 7RH, UK.
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.
Insights
Bayesian trials significantly reduce sample size needs in rare disease studies by using informative priors. Frequentist trials often fail to meet recruitment targets, highlighting Bayesian methods as underutilized solutions.
Area of Science:
- Clinical Trials Methodology
- Biostatistics
- Rare Disease Research
Background:
- Rare disease and small population trials face challenges with large sample size requirements for frequentist designs.
- Restricted patient recruitment in these trials necessitates innovative design and analysis approaches.
Purpose of the Study:
- To investigate the design and analysis methods employed in rare disease and small population trials with recruitment limitations.
- To compare the efficiency of Bayesian versus frequentist approaches in these challenging trial settings.
Main Methods:
- A targeted review of Phase II-IV Randomized Controlled Trials (RCTs) published since 2009 was conducted.
- Databases searched included EMBASE and MEDLINE, focusing on trials reporting 'rare disease' or 'small population' in their title or abstract.
- Eligible trials were analyzed for their design characteristics, statistical methods, and recruitment success.
Main Results:
- Out of 6,128 screened articles, 64 trials were eligible (60 frequentist, 4 Bayesian).
- Frequentist trials often failed to meet planned sample sizes, recruiting a mean of 6.6% below target.
- Bayesian trials, particularly those using informed priors, demonstrated substantial reductions (30-2400%) in participant requirements compared to frequentist frameworks.
Conclusions:
- Bayesian methods, especially with informative priors, offer a significant advantage in reducing sample size for rare disease and small population trials.
- Frequentist trials frequently under-recruit, indicating a mismatch between design assumptions and practical execution.
- Bayesian approaches present promising, yet underutilized, solutions for overcoming recruitment barriers in rare disease research.
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