Cluster analysis for repeated data with dropout: Sensitivity analysis using a distal event
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. ; The authors gratefully acknowledge support from IAP research Network P7/06 of the Belgian Government (Belgian Science Policy). We are grateful to the Department of Clinical Chemistry, Maastricht University Medical Center (MUMC), Maastricht, for kind permission to use the AAA data.