Repeated measurements are widely encountered in medical or pharmaceutical studies, which can be analyzed by both longitudinal data and functional data analysis methods, particularly when the underlying process is continuous and the number of measurement points is not too small. Motivated by real problems of clustering patient profiles in clinical trials, this paper gives an overview of the clustering methods for repeated measurement data and compares three longitudinal data methods and two functional data methods with extensive simulation studies. Methods with appropriate properties are applied to the real data to produce interpretable results.
[1]East China Normal Univ, Key Lab Adv Theory & Applicat Stat & Data Sci MOE, Sch Stat, 3663 North Zhongshan Rd, Shanghai, Peoples R China;
[2]Fudan Univ, Sch Data Sci, Shanghai, Peoples R China
(8.0+极速模式)