A comparison of joint dichotomization and single dichotomization of interacting variables to discriminate a disease outcome

Nelson Sybil Prince (), Ramakrishnan Viswanathan, Nietert Paul, Kamen Diane, Ramos Paula and Wolf Bethany
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Nelson Sybil Prince: Department of Mathematics, Washington and Lee University, 204 W Washington St, Lexington, VA 24450, USA
Ramakrishnan Viswanathan: Department of Public Health Sciences, Medical University of South Carolina
Nietert Paul: Department of Public Health Sciences, Medical University of South Carolina
Kamen Diane: Department of Medicine, Medical University of South Carolina
Ramos Paula: Department of Medicine, Medical University of South Carolina
Wolf Bethany: Department of Public Health Sciences, Medical University of South Carolina

The International Journal of Biostatistics, 2022, vol. 18, issue 2, 613-625

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.

Keywords: cutpoints; dichotomization; disease prediction; odds ratio; Youden’s (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1515/ijb-2021-0071

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