Optimal Change Point Detection in Longitudinal Data: A Two-Step Approach

Music City Center 

Addressing non-linear trends in longitudinal data with irregular measurements involves fitting linear trends in segments joined at fixed times, known as change points (CPs). Methods to determine CP locations and numbers for piecewise linear mixed effects models are scarce, standard software lacks adequate algorithms, and the RE-EM tree may emphasize the intercept over the slope of trends. The Segmented package in R is a powerful tool for analyzing segmented relationships and identifying changepoints in regression models, though it has limited use for longitudinal data. This study explores the application of segmented methods combined with grid search to accurately identify CPs in longitudinal data. The proposed two-step approach first estimates the number and initial locations of CPs without considering within-subject correlation. In the second step, these locations are refined by accounting for within-subject correlation through a grid search in a piecewise linear mixed effects model. The findings demonstrate that this combined method effectively optimizes CP locations and outperforms the RE-EM tree, in fitting non-linear early childhood growth patterns measured by BMIz.

Change Point

Non-linear Curves


Longitudinal Data

RE-EM tree

piecewise linear mixed effects model

segmented methods 

Section on Statistical Learning and Data Science