SynthesisPharmaceutical statistics2021
Prior distributions for variance parameters in a sparse-event meta-analysis of a few small trials.
Synthesis in Pharmaceutical statistics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- A systematic review and meta-analysis of bovine tuberculosis occurrence and burden in Bangladesh, 1970-2023.Epidemiology and infection · 2024Pooled it
- Bayesian Approaches for Confirmatory Trials in Rare Diseases: Opportunities and Challenges.International journal of environmental research and public health · 2021Pooled it
- Prior distributions for variance parameters in a sparse-event meta-analysis of a few small trials.Pharmaceutical statistics · 2021Pooled it
- Bayesian reanalysis of early remdesivir for the treatment of COVID-19 in outpatients with high risk of progression to severe disease.PloS one · 2026Trial
- Bayes factor hypothesis testing in meta-analyses: Practical advantages and methodological considerations.Research synthesis methods · 2026Review
- Random-effects meta-analysis models for pooling rare events data: a comparison between frequentist and bayesian methods.BMC medical research methodology · 2025Article
- Combined assessment of early and late-phase outcomes in orphan drug development.Statistics in medicine · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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(τ
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.