Broken Stick Model for Irregular Longitudinal Data

Many longitudinal studies collect data that have irregular observation times, often requiring the application of linear mixed models with time-varying outcomes. This paper presents an alternative that splits the quantitative analysis into two steps. The first step converts irregularly observed data into a set of repeated measures through the...

A blended distance to define "people-like-me"

Curve matching is a prediction technique that relies on predictive mean matching, which matches donors that are most similar to a target based on the predictive distance. Even though this approach leads to high prediction accuracy, the predictive distance may make matches look unconvincing, as the profiles of the matched donors can substantially...

Curve matching to predict growth in patients receiving growth hormone therapy: An interpretable & explainable method

Curve matching may be used to predict growth outcomes using data of patients whose growth curves resemble those of a new patient with growth hormone deficiency (GHD) and those born small for gestational age (SGA). We aimed to investigate the validity of curve matching to predict growth in patients with GHD and those born SGA receiving...

Curve matching: A data-driven technique to improve individual prediction of childhood growth

Longitudinal growth data are valuable for predicting and interpreting future growth of individual children. This note explores the idea of 'curve matching', a new technique to improve prediction of future growth of an individual child. The key idea is to find existing children in existing databases that are similar to the current child. The...