Key result
Splines or fractional polynomials avoid the negative consequences of categorizing continuous variables in healthcare research.
Why the study?
Proper handling of continuous variables is crucial in healthcare research, but inadequate methods are commonly used.
Key points are not available for this paper at this time.
Design
Practical guide
Urges caution interpreting studies categorizing continuous predictors; leaves open optimal spline implementation across datasets.
Proper handling of continuous variables is crucial in healthcare research, for example, within regression modelling for descriptive, explanatory, or predictive purposes. However, inadequate methods are commonly used. This article highlights the importance of appropriately handling continuous variables, and illustrates the consequences of categorisation. This article also explains why assuming a linear relationship between the independent and dependent variable might be inappropriate, and describes how to use splines or fractional polynomials to model non-linear relationships.
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Lopez-Ayala et al. (2025) reported a review. Using splines or fractional polynomials to model non-linear relationships avoids the negative consequences of categorizing continuous variables in healthcare research.
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