ArticleContemporary clinical trials communications2018
Interval estimation of the overall treatment effect in a meta-analysis of a few small studies with zero events.
Article in Contemporary clinical trials communications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 4 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, 4 syntheses or guidelines pooled it, 19 citations in OpenAlex.
- Bayesian estimation and testing in random-effects meta-analysis of rare binary events allowing for flexible group variability.Statistics in medicine · 2023Pooled it
- Bayesian Meta-Analysis for Binary Data and Prior Distribution on Models.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
- Pooled it
- Bayesian heterogeneity in a meta-analysis with two studies and binary data.Journal of applied statistics · 2023Article
- Combined assessment of early and late-phase outcomes in orphan drug development.Statistics in medicine · 2021Article
- Applicability and added value of novel methods to improve drug development in rare diseases.Orphanet journal of rare diseases · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
When a meta-analysis consists of a few small trials that report zero events, accounting for heterogeneity in the (interval) estimation of the overall effect is challenging. Typically, we predefine meta-analytical methods to be employed. In practice, data poses restrictions that lead to deviations from the pre-planned analysis, such as the presence of zero events in at least one study arm. We aim to explore heterogeneity estimators behaviour in estimating the overall effect across different levels of sparsity of events. We performed a simulation study that consists of two evaluations. We considered an overall comparison of estimators unconditional on the number of observed zero cells and an additional one by conditioning on the number of observed zero cells. Estimators that performed modestly robust when (interval) estimating the overall treatment effect across a range of heterogeneity assumptions were the Sidik-Jonkman, Hartung-Makambi and improved Paul-Mandel. The relative performance of estimators did not materially differ between making a predefined or data-driven choice. Our investigations confirmed that heterogeneity in such settings cannot be estimated reliably. Estimators whose performance depends strongly on the presence of heterogeneity should be avoided. The choice of estimator does not need to depend on whether or not zero cells are observed.
Indexed as
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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.