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Summary: In 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(τ2) 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. [ABSTRACT FROM AUTHOR]

We aimed to estimate the overall apparent prevalence, true prevalence, and the spatial, temporal, and test-specific burden of bovine tuberculosis in Bangladesh. PubMed, Web of Science, Scopus, Google Scholar, and BanglaJOL were searched for bovine tuberculosis publications in Bangladesh from 1 January 1970 to 23 June 2023. Of 142 articles screened, systematic review and meta-analysis were performed on 22 (15.5%) articles. The apparent estimated bovine tuberculosis prevalence was 7%. The apparent Bayesian pooled mean bovine tuberculosis prevalences based on caudal fold test and single intradermal comparative tuberculin test were 7.83% and 9.89%, respectively, and the true pooled mean prevalences were 10.39% and 10.48%, respectively. Targeted interventions are recommended for districts with higher prevalence to effectively reduce the bovine tuberculosis burden in those areas. Current diagnostic practices employed in Bangladesh may not accurately reflect the bovine tuberculosis burden. Our findings highlight the need for better diagnostic tools and supplemental testing methods to ensure accurate diagnosis and surveillance. Efforts should prioritize obtaining 'true' prevalence estimates corrected for misclassification bias, rather than relying solely on apparent prevalence. Underestimating the bovine tuberculosis burden could result in inadequate resource allocation and hinder the implementation of effective control measures.

Simulation studies to evaluate performance of statistical methods require a well-specified data-generating model. Details of these models are essential to interpret the results and arrive at proper conclusions. A case in point is random-effects meta-analysis of dichotomous outcomes. We reviewed a number of simulation studies that evaluated approximate normal models for meta-analysis of dichotomous outcomes, and we assessed the data-generating models that were used to generate events for a series of (heterogeneous) trials. We demonstrate that the performance of the statistical methods, as assessed by simulation, differs between these 3 alternative data-generating models, with larger differences apparent in the small population setting. Our findings are relevant to multilevel binomial models in general., (Copyright © 2017 John Wiley & Sons, Ltd.)