Cluster analysis for repeated data with dropout: Sensitivity analysis using a distal event

Abstract

Degeneration of the aortic wall becomes life-threatening when the risk of rupture increases. Cluster analysis on repeated measures of the diameter of the artery revealed two subgroups of patients included in a surveillance program. These results were obtained under the assumption of missingness at random. In this article, we study the vulnerability of the cluster analysis results - the estimated trajectories and the posterior membership probabilities - by applying different missing-data models for non-ignorable dropout, as proposed by Muthen et al. (2011) to the growth of the diameter of the artery.