Open AccessUnderstanding the effect of categorization of a continuous predictor with application to neuro-oncology
Author(s) -
R. Gupta,
Courtney N. Day,
W. Oliver Tobin,
Cynthia S. Crowson
Publication year - 2021
Publication title -
neuro-oncology practice
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.666
H-Index - 14
eISSN - 2054-2585
pISSN - 2054-2577
DOI - 10.1093/nop/npab049
Subject(s) - categorical variable , continuous variable , categorization , medicine , oncology , predictive power , radiomics , association (psychology) , glioma , psychology , computer science , artificial intelligence , machine learning , radiology , psychotherapist , philosophy , epistemology , cancer research
Many neuro-oncology studies commonly assess the association between a prognostic factor (predictor) and disease or outcome, such as the association between age and glioma. Predictors can be continuous (eg, age) or categorical (eg, race/ethnicity). Effects of categorical predictors are frequently easier to visualize and interpret than effects of continuous variables. This makes it an attractive, and seemingly justifiable, option to subdivide the continuous predictors into categories (eg, age <50 years vs age ≥50 years). However, this approach results in loss of information (and power) compared to the continuous version. This review outlines the use cases for continuous and categorized predictors and provides tips and pitfalls for interpretation of these approaches.