Dichotomization of continuous measurements using generalized additive modelling--application in predicting intrapartum caesarean delivery.

In prediction model development, continuous variables are often dichotomized at empirically chosen thresholds to simplify calculations and facilitate decision making. However, these choices are often made in the absence of estimated covariate effects and may be inaccurate, thus weakening the models. To improve this approach, generalized additive modelling that allows non-parametric estimation of the true covariate effects is used for threshold selection. In this study, this approach is illustrated by development of prediction models for intrapartum Caesarean deliveries. The prediction performance of the models thus developed is significantly better than that developed using empirically chosen thresholds for dichotomization.