Prediction intervals for future BMI values of individual children - a non-parametric approach by quantile boosting

We avoid distributional assumptions by directly modelling the borders of PIs by additive quantile regression, estimated by boosting. We point out the concept of conditional coverage to prove the accuracy of PIs. As conditional coverage can hardly be evaluated in practical applications, we conduct a simulation study before fitting child- and covariate-specific PIs for future BMI values and BMI patterns for the present data.The results of our simulation study suggest that PIs fitted by quantile boosting cover future observations with the predefined coverage probability and outperform the benchmark approach. For the prediction of future BMI values, quantile boosting automatically selects informative covariates and adapts to the age-specific skewness of the BMI distribution. The lengths of the estimated PIs are child-specific and increase, as expected, with the age of the child.Quantile boosting is a promising approach to construct PIs with correct conditional coverage in a non-parametric way. It is in particular suitable for the prediction of BMI patterns depending on covariates, since it provides an interpretable predictor structure, inherent variable selection properties and can even account for longitudinal data structures.Childhood obesity is more and more becoming a problem of epidemic dimensions in modern societies [1,2]. The body mass index (BMI) has proved to be a reliable measure to assess childhood obesity and can also be seen as an indicator for obesity in adulthood [3,4]. Therefore, the prediction of future BMI values for individual children may be used as a warning bell for clinicians, parents and children. Predicting future BMI values raises awareness for problems to come - as long as they are still avoidable - and can thus lower the risk of later obesity.In this setting, we focus on obtaining reliable predictions for future BMI values of children. Prediction intervals (PIs) offer information on the expected variability by providing not only a point predi