Abstract
SummaryIn rare diseases, typically only a small number of patients are available for a randomized clinical trial. Nevertheless, it is not uncommon that more than one study is performed to evaluate a (new) treatment. Scarcity of available evidence makes it particularly valuable to pool the data in a meta‐analysis. When the primary outcome is binary, the small sample sizes increase the chance of observing zero events. The frequentist random‐effects model is known to induce bias and to result in improper interval estimation of the overall treatment effect in a meta‐analysis with zero events. Bayesian hierarchical modeling could be a promising alternative. Bayesian models are known for being sensitive to the choice of prior distributions for between‐study variance (heterogeneity) in sparse settings. In a rare disease setting, only limited data will be available to base the prior on, therefore, robustness of estimation is desirable. We performed an extensive and diverse simulation study, aiming to provide practitioners with advice on the choice of a sufficiently robust prior distribution shape for the heterogeneity parameter. Our results show that priors that place some concentrated mass on small τ values but do not restrict the density for example, the Uniform(−10, 10) heterogeneity prior on the log(τ 2) scale, show robust 95% coverage combined with less overestimation of the overall treatment effect, across varying degrees of heterogeneity. We illustrate the results with meta‐analyzes of a few small trials.
Highlights
IntroductionTo reach firm conclusions, randomized controlled trials (RCTs) commonly require large enough sample sizes, but this is not always feasible for (very) rare diseases[1] in which the limited patient population leads naturally to small RCTs.[2] In RCTs, dichotomous outcomes are common as they facilitate straightforward clinical interpretation for both efficacy and Abbreviations: RCTs, randomized Clinical Trials; MA, meta-analysis; logOR, log odds ratio; CrI, credible interval
To reach firm conclusions, randomized controlled trials (RCTs) commonly require large enough sample sizes, but this is not always feasible for rare diseases[1] in which the limited patient population leads naturally to small RCTs.[2]
In RCTs, dichotomous outcomes are common as they facilitate straightforward clinical interpretation for both efficacy and Abbreviations: RCTs, randomized Clinical Trials; MA, meta-analysis; logOR, log odds ratio; CrI, credible interval
Summary
To reach firm conclusions, randomized controlled trials (RCTs) commonly require large enough sample sizes, but this is not always feasible for (very) rare diseases[1] in which the limited patient population leads naturally to small RCTs.[2] In RCTs, dichotomous outcomes are common as they facilitate straightforward clinical interpretation for both efficacy and Abbreviations: RCTs, randomized Clinical Trials; MA, meta-analysis; logOR, log odds ratio; CrI, credible interval. When combined with small sample sizes and low to moderate event rates, such outcomes lead to a large probability of observing zero events on one or more trial arms. Even in rare diseases usually more than one trial is available for evaluating a (new) treatment.[3,4] The small sample sizes make it valuable to pool the data in a meta-analysis (MA). To synthesize available RCTs, the standard random-effects MA model is usually applied, known as the normal-normal hierarchical model
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
