ArticleJAMA2023
Evidence of Lack of Treatment Efficacy Derived From Statistically Nonsignificant Results of Randomized Clinical Trials.
Article in JAMA, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- Perioperative lung expansion and pulmonary outcomes after open abdominal surgery versus usual care in the USA (PRIME-AIR): a multicentre, randomised, controlled, phase 3 trial.The Lancet. Respiratory medicine · 2025Trial
- Bayesian probabilistic statements for communicating clinical trial results.BMC medicine · 2026Article
- Review
- Exploratory analyses of clinical trials - what can they tell us and what are the potential pitfalls?Nature communications · 2026Article
- Trial Analysis and Interpretation in Critical Care Using the Evidential (Likelihood) Approach: Rationale and Practical Considerations.American journal of respiratory and critical care medicine · 2025Review
- Bayesian Reanalysis of Statistically Nonsignificant Outcomes in Plastic Surgery Clinical Trials.Plastic and reconstructive surgery. Global open · 2024Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Importance: Many randomized clinical trials yield statistically nonsignificant results. Such results are difficult to interpret within the dominant statistical framework. Objective: To estimate the strength of evidence in favor of the null hypothesis of no effect vs the prespecified effectiveness hypothesis among nonsignificant primary outcome results of randomized clinical trials by application of the likelihood ratio. Design, Setting, and Participants: Cross-sectional study of statistically nonsignificant results for primary outcomes of randomized clinical trials published in 6 leading general medical journals in 2021. Outcome measures: The likelihood ratio for the null hypothesis of no effect vs the effectiveness hypothesis stated in the trial protocol (alternate hypothesis). The likelihood ratio quantifies the support that the data provide to one hypothesis vs the other. Results: In 130 articles that reported 169 statistically nonsignificant results for primary outcomes, 15 results (8.9%) favored the alternate hypothesis (likelihood ratio, <1), and 154 (91.1%) favored the null hypothesis of no effect (likelihood ratio, >1). For 117 (69.2%), the likelihood ratio exceeded 10; for 88 (52.1%), it exceeded 100; and for 50 (29.6%), it exceeded 1000. Likelihood ratios were only weakly correlated with P values (Spearman r, 0.16; P = .045). Conclusions: A large proportion of statistically nonsignificant primary outcome results of randomized clinical trials provided strong support for the hypothesis of no effect vs the alternate hypothesis of clinical efficacy stated a priori. Reporting the likelihood ratio may improve the interpretation of clinical trials, particularly when observed differences in the primary outcome are statistically nonsignificant.
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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.