Spline Regression

In regression modeling when we include a continuous predictor variable in our model, either as the main exposure of interest or as a confounder, we are making the assumption that the relationship between the predictor variable and the outcome is linear. In other words, a one unit increase in the predictor variable is associated with a fixed difference in the outcome. Thus, we make no distinction between a one unit increase in the predictor variable near the minimum value and a one unit increase in the predictor variable near the maximum value. This assumption of linearity may not always be true, and may lead to an incorrect conclusion about the relationship between the exposure and outcome, or in the case of a confounder that violates the linearity assumption, may lead to residual confounding. Spline regression is one method for testing non-linearity in the predictor variables and for modeling non-linear functions.





Dose-response and trend analysis in epidemiology: alternatives to categorical analysis

A practical guide to dose-response analyses and risk assessment in occupational epidemiology

Comparing penalized splines and fractional polynomials for flexible modelling of the effects of continuous predictor variables

Flexible regression models with cubic splines

The question of nonlinearity in the dose-response relation between particulate matter air pollution and mortality

Splines for trend analysis and continuous confounder control


Childhood, adolescent and early adult body mass index in relation to adult mortality: results from the British 1946 birth cohort

Psychosocial factors and coronary calcium in adults without clinical cardiovascular disease

Spirometric reference values from a sample of the general U.S. population

Amount of leisure-time physical activity and risk of nonfatal myocardial infarction