ArticleBMC medical research methodology2025
Beyond prediction intervals in meta-analysis: reporting the expected proportion of comparable studies with clinically relevant benefit or harm.
Article in BMC medical research methodology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis 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
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Effects of exercise on physical function and pain in adults with rheumatoid arthritis: an exit meta-analysis of randomized controlled trials.Clinical rheumatology · 2026Pooled it
- Disentangling the GRADE Domains of Inconsistency and Imprecision: An Approach Based on a Meta-Research Study.Cochrane evidence synthesis and methods · 2026Article
- Article
- Osimertinib for Patients With EGFR-Mutated Non-Small Cell Lung Cancer: Current Evidence.Clinical Medicine Insights. Oncology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn a meta-analysis where the effect size varies substantially between studies it is important to report the extent of the variation. Critically, we want to know if the treatment is always helpful or sometimes harmful. The statistic that addresses this is the prediction interval (PI), which gives the range of true effects for all studies comparable to those in the meta-analysis.
methodsIn addition to the PI's upper and lower limits, we propose to report the expected proportion of comparable studies that are expected to have an effect in a given range. If we define for example thresholds corresponding to minimal clinically important benefit and harm, we can report the expected proportion of comparable studies where the true effect is expected to exceed these thresholds.
resultsWe apply our approach to two Cochrane Reviews assessing a dichotomous and a continuous outcome: caesarean section and health-related quality of life. This article shows how to plot the distribution of true study effects highlighting the expected proportion of comparable studies where the true effect is clinically beneficial or harmful. We also offer suggestions for how to report this information in scientific articles.
conclusionIn addition to PIs, reporting the expected proportion of comparable studies with relevant benefit or harm as supplementary information could help physicians and other decision-makers to understand the potential utility of an intervention. However, these metrics must be interpreted with caution because the estimate of the between‑study heterogeneity [Formula: see text] may be imprecise when data are limited.
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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.