ArticleJournal of general internal medicine2022
Approaches to Assessing and Adjusting for Selective Outcome Reporting in Meta-analysis.
Article in Journal of general internal medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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.
- Correcting what cannot be corrected: rethinking publication bias analysis methods in clinical meta-analyses.Korean journal of anesthesiology · 2026Review
- Compliance of systematic reviews and meta-analyses in ophthalmology with the PRISMA statement: an AI-based assessment and longitudinal comparison with 2017 data.BMC medical research methodology · 2026Article
- The threat of serious outcome reporting bias in randomized controlled trials on acute ischemic stroke to evidence synthesis: a meta-epidemiological study.Cardiovascular diagnosis and therapy · 2025Review
- ZNF529 up-regulation speeds up progression and induces tyrosine kinase inhibitor resistance in hepatocellular carcinoma.Journal of gastrointestinal oncology · 2025Article
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSelective or non-reporting of study outcomes results in outcome reporting bias.
objectiveWe sought to develop and assess tools for detecting and adjusting for outcome reporting bias.
designUsing data from a previously published systematic review, we abstracted whether outcomes were reported as collected, whether outcomes were statistically significant, and whether statistically significant outcomes were more likely to be reported. We proposed and tested a model to adjust for unreported outcomes and compared our model to three other methods (Copas, Frosi, trim and fill). Our approach assumes that unreported outcomes had a null intervention effect with variance imputed based on the published outcomes. We further compared our approach to these models using simulation, and by varying levels of missing data and study sizes.
resultsThere were 286 outcomes reported as collected from 47 included trials: 142 (48%) had the data provided and 144 (52%) did not. Reported outcomes were more likely to be statistically significant than those collected but for which data were unreported and for which non-significance was reported (RR, 2.4; 95% CI, 1.9 to 3.0). Our model and the Copas model provided similar decreases in the pooled effect sizes in both the meta-analytic data and simulation studies. The Frosi and trim and fill methods performed poorly. LIMITATIONS: Single intervention of a single disease with only randomized controlled trials; approach may overestimate outcome reporting bias impact.
conclusionThere was evidence of selective outcome reporting. Statistically significant outcomes were more likely to be published than non-significant ones. Our simple approach provided a quick estimate of the impact of unreported outcomes on the estimated effect. This approach could be used as a quick assessment of the potential impact of unreported outcomes.
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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.