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

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.

SUBMITTER: Nelson SP

PROVIDER: S-EPMC10198136 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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

Nelson Sybil Prince SP Ramakrishnan Viswanathan V Nietert Paul P Kamen Diane D Ramos Paula P Wolf Bethany B

The international journal of biostatistics 20220510 2

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 ...[more]

PMID: 35536987

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Project description:Many machine learning and pattern classification methods have been applied to the diagnosis of Alzheimer's disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI). Recently, rather than predicting categorical variables as in classification, several pattern regression methods have also been used to estimate continuous clinical variables from brain images. However, most existing regression methods focus on estimating multiple clinical variables separately and thus cannot utilize the intrinsic useful correlation information among different clinical variables. On the other hand, in those regression methods, only a single modality of data (usually only the structural MRI) is often used, without considering the complementary information that can be provided by different modalities. In this paper, we propose a general methodology, namely multi-modal multi-task (M3T) learning, to jointly predict multiple variables from multi-modal data. Here, the variables include not only the clinical variables used for regression but also the categorical variable used for classification, with different tasks corresponding to prediction of different variables. Specifically, our method contains two key components, i.e., (1) a multi-task feature selection which selects the common subset of relevant features for multiple variables from each modality, and (2) a multi-modal support vector machine which fuses the above-selected features from all modalities to predict multiple (regression and classification) variables. To validate our method, we perform two sets of experiments on ADNI baseline MRI, FDG-PET, and cerebrospinal fluid (CSF) data from 45 AD patients, 91 MCI patients, and 50 healthy controls (HC). In the first set of experiments, we estimate two clinical variables such as Mini Mental State Examination (MMSE) and Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog), as well as one categorical variable (with value of 'AD', 'MCI' or 'HC'), from the baseline MRI, FDG-PET, and CSF data. In the second set of experiments, we predict the 2-year changes of MMSE and ADAS-Cog scores and also the conversion of MCI to AD from the baseline MRI, FDG-PET, and CSF data. The results on both sets of experiments demonstrate that our proposed M3T learning scheme can achieve better performance on both regression and classification tasks than the conventional learning methods.

Dataset Information

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

Publications

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

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