ArticleThe international journal of biostatistics2022
A comparison of joint dichotomization and single dichotomization of interacting variables to discriminate a disease outcome.
Article in The international journal of biostatistics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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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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.
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Who cites it
1 citing paper in PubMed, 1 citations in OpenAlex.
- Explainable machine learning differentiates necrotizing fasciitis and osteomyelitis via routine blood biomarkers.NPJ digital medicine · 2026Article
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
Dichotomization is often used on clinical and diagnostic settings to simplify interpretation. For example, a person with systolic and diastolic blood pressure above 140 over 90 may be prescribed medication. Blood pressure as well as other factors such as age and cholesterol and their interactions may lead to increased risk of certain diseases. When using a dichotomized variable to determine a diagnosis, if the interactions with other variables are not considered, then an incorrect threshold for the continuous variable may be selected. In this paper, we compare single dichotomization with joint dichotomization; the process of simultaneously optimizing cutpoints for multiple variables. A simulation study shows that simultaneous dichotomization of continuous variables is more accurate in recovering both 'true' thresholds given they exist.
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Registered trials
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