ArticleF1000Research2018
Smaller clinical trials for decision making; a case study to show p-values are costly.
Article in F1000Research, 2018. 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
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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
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- How fragile the positive results of Chinese herbal medicine randomized controlled trials on irritable bowel syndrome are?BMC complementary medicine and therapies · 2024Pooled it
- Is a novel diagnostic pathway for cardiology outpatient clinics in Singapore lower cost than existing practice: a cost modelling study.BMJ open · 2022Article
- Using simulation studies to evaluate statistical methods.Statistics in medicine · 2019Article
- Smaller clinical trials for decision making; a case study to show p-values are costly.F1000Research · 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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundClinical trials might be larger than needed because arbitrary levels of statistical confidence are sought in the results. Traditional sample size calculations ignore the marginal value of the information collected for decision making. The statistical hypothesis testing objective is misaligned with the goal of generating information necessary for decision-making. The aim of the present study was to show that for a case study clinical trial designed to test a prior hypothesis against an arbitrary threshold of confidence more participants were recruited than needed to make a good decision about adoption.
methodsWe used data from a recent RCT powered for traditional rules of statistical significance. The data were also used for an economic analysis to show the intervention led to cost-savings and improved health outcomes. Adoption represented a sensible investment for decision-makers. We examined the effect of reducing the trial's sample size on the results of the statistical hypothesis-testing analysis and the conclusions that would be drawn by decision-makers reading the economic analysis.
resultsAs the sample size reduced it became more likely that the null hypothesis of no difference in the primary outcome between groups would fail to be rejected. For decision-makers reading the economic analysis, reducing the sample size had little effect on the conclusion about whether to adopt the intervention. There was always high probability the intervention reduced costs and improved health.
conclusionsDecision makers managing health services are largely invariant to the sample size of the primary trial and the arbitrary p-value of 0.05. If the goal is to make a good decision about whether the intervention should be adopted widely, then that could have been achieved with a much smaller trial. It is plausible that hundreds of millions of research dollars are wasted each year recruiting more participants than required for RCTs.
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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.