ArticleTrials2023
The small trial problem.
Article in Trials, 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
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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, 7 citations in OpenAlex.
- Explainable machine learning differentiates necrotizing fasciitis and osteomyelitis via routine blood biomarkers.NPJ digital medicine · 2026Article
- Unveiling the paradox: a commentary on AI's unintended consequences in organ donation.BMJ open quality · 2025Article
- GLP-1 Receptor Agonists and Cardiac Health: Insights from a Systematic Review.Cardiovascular drugs and therapy · 2025Article
- Using protection motivation theory to support patient adherence in healthcare settings: A scoping review.PloS one · 2025Article
- Characteristics of pediatric interventional drug trials registered between 2015 and 2024 on ClinicalTrials.gov.Frontiers in pediatrics · 2025Article
- Importance of sample size determination for randomized controlled clinical trials for coronavirus disease 2019 antiviral therapies.Pediatric investigation · 2024Article
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 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMany randomized trials that aim to assess new or commonly used medical or surgical interventions have been so small that the validity of conclusions becomes questionable.
methodsWe illustrate the small trial problem using the power calculation of five Cochrane-reviewed studies that compared vertebroplasty versus placebo interventions. We discuss some of the reasons why the statistical admonition not to dichotomize continuous variables may not apply to the calculation of the number of patients required for trials to be meaningful.
resultsPlacebo-controlled vertebroplasty trials planned to recruit between 23 and 71 patients per group. Four of five studies used the standardized mean difference of a continuous pain variable (centimeters on the visual analog scale (VAS)) to plan implausibly small trials. What is needed is not a mean effect at the population level but a measure of efficacy at the patient level. Clinical practice concerns the care of individual patients that vary in many more respects than the variation around the mean of a single selected variable. The inference from trial to practice concerns the frequency of success of the experimental intervention performed one patient at a time. A comparison of the proportions of patients reaching a certain threshold is a more meaningful method that appropriately requires larger trials.
conclusionMost placebo-controlled vertebroplasty trials used comparisons of means of a continuous variable and were consequently very small. Randomized trials should instead be large enough to account for the diversity of future patients and practices. They should offer an evaluation of a clinically meaningful number of interventions performed in various contexts. Implications of this principle are not specific to placebo-controlled surgical trials. Trials designed to inform practice require a per-patient comparison of outcomes and the size of the trial should be planned accordingly.
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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.