ArticleBMC medical research methodology2025
"Cake causes herpes?" - promiscuous dichotomisation induces false positives.
Article in BMC medical research methodology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Quantifying new threats to health and biomedical literature integrity from rapidly scaled publications and problematic research.Journal of clinical epidemiology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
backgroundContinuous biomedical data is often dichotomized into two or more groups for analysis, despite long-standing warnings from statisticians that this constitutes bad practice. This dichotomisation is typically discouraged because it reduces statistical power and may obscure important trends. This paper considers another reason to discourage this practice: that dichotomisation is a powerful tool to manipulate data, as dichotomising at an arbitrary yet flexible threshold (which we term 'promiscuous dichotomisation') represents a powerful researcher degree of freedom.
methodsThe motivating question is how probable is it that given a set of uniformly distributed data a threshold can be engineered to produce the illusion of a true effect when none exists? To estimate this, we employed both analytical approaches and Monte-Carlo simulation approaches to quantify the expected number of spurious findings that could arise from manipulating a dichotomous threshold for an arbitrary data set. We also illustrate an example of this with NHANES data, showing how a spurious relationship between blood glucose and herpes status could be engineered.
resultsFor even a relatively small sample of [Formula: see text], a false positive rate of [Formula: see text] can be observed, rising to over [Formula: see text] if low counts scenarios are not excluded. With larger samples even with low-count exclusion, false positive rates in excess of [Formula: see text] for [Formula: see text] and [Formula: see text] for [Formula: see text] are possible, climbing to in excess of [Formula: see text] and [Formula: see text] respectively if low-count scenarios were not excluded. For most configurations, manipulation of thresholds was a highly viable methods of crafting a false positive result.
conclusionsIt is likely that manipulating cut-off points in measured variables represents a significant source of data manipulation in published science, and the ease of access of larger health databases means this is an issue that is likely to grow in severity. We discuss implications of this, and means of identifying potential promiscuous dichotomisation.
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What Socratic 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 Socratic graph.