ArticleBMC medical research methodology2019
True and false positive rates for different criteria of evaluating statistical evidence from clinical trials.
Article in BMC medical research methodology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses 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.
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Who cites it
10 citing papers in PubMed, 2 syntheses or guidelines pooled it.
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- Shared and distinct brain fMRI response during performance of working memory tasks in adult patients with schizophrenia and major depressive disorder.Human brain mapping · 2021Pooled it
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- A deep-learning approach for segmentation of liver tumors in magnetic resonance imaging using UNet+.BMC cancer · 2023Article
- Analysis of lung cancer risk factors from medical records in Ethiopia using machine learning.PLOS digital health · 2023Article
- The Functional Erythropoetin rs1617640 Gene Polymorphism does not Affect Life Expectancy of Patients with Peripheral Arterial Disease.Reviews in cardiovascular medicine · 2023Article
- The failure of drug repurposing for COVID-19 as an effect of excessive hypothesis testing and weak mechanistic evidence.History and philosophy of the life sciences · 2022Article
- Eating and Control Styles Axis in Mentalisation-Based Psychotherapy in Eating Disorders: A Randomised Clinical Trial.Frontiers in psychiatry · 2022Article
- The ErythropoetinGenes · 2020Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundUntil recently a typical rule that has often been used for the endorsement of new medications by the Food and Drug Administration has been the existence of at least two statistically significant clinical trials favoring the new medication. This rule has consequences for the true positive (endorsement of an effective treatment) and false positive rates (endorsement of an ineffective treatment).
methodsIn this paper, we compare true positive and false positive rates for different evaluation criteria through simulations that rely on (1) conventional p-values; (2) confidence intervals based on meta-analyses assuming fixed or random effects; and (3) Bayes factors. We varied threshold levels for statistical evidence, thresholds for what constitutes a clinically meaningful treatment effect, and number of trials conducted.
resultsOur results show that Bayes factors, meta-analytic confidence intervals, and p-values often have similar performance. Bayes factors may perform better when the number of trials conducted is high and when trials have small sample sizes and clinically meaningful effects are not small, particularly in fields where the number of non-zero effects is relatively large.
conclusionsThinking about realistic effect sizes in conjunction with desirable levels of statistical evidence, as well as quantifying statistical evidence with Bayes factors may help improve decision-making in some circumstances.
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