Table 1.

Methods to approximate true non-linear effects

Method	Explanation	Advantages	Disadvantages
Categorization	The confounder is grouped (e.g. on pre-specified percentile values such as quartiles) and subsequently the outcome is regressed on the exposure and the now categorical confounding variable	Easy to apply	Homogeneity of the effects is assumed within groups, resulting in severe loss of information and possibly residual confounding
Non-linear terms	The outcome is regressed on the confounder and the non-linear term of that same confounder, e.g., a quadratic term	Easy to apply Adding non-linear order terms increases the flexibility of the model	Coefficients are difficult to interpret*
Linear spline regression	First, the confounding variable is categorized and subsequently a first power function is fitted for each category separately. After fitting the spline functions these are added to the regression model	Good approximation of the true effect Coefficients are easy to interpret
Restricted cubic spline regression	Same as linear spline regression, but instead a more flexible third power function is fitted for each category separately. To avoid instability in the tails where there’s not much data, restricted cubic splines are often used where at the tails a line is fitted rather than a curve.	Good approximation of the true effect Adding splines increases the flexibility of the model	Coefficients are difficult to interpret*

^* This is not a hindrance when these methods are used to model non-linear confounder-exposure or confounder-outcome associations as the corresponding coefficients will not be interpreted