Patient stratification using longitudinal data - Application of latent class mixed models

Abstract

Analysis of longitudinal data in medical research is becoming increasingly important, in particular for the identification of patient subgroups, as the focus of medical research is shifting toward personalised medicine. Here we present the use of a statistical learning approach for the identification of subgroups of hypertension patients demonstrating different patterns of response to treatment. This method, applied to large-scale patient-level data, has identified three such groups found to be associated with different clinical characteristics. We further consider the utility of this method in medical research by comparison to the application in two additional studies.