title: Clustering of longitudinal data by using an extended baseline: A new method for treatment efficacy clustering in longitudinal data.
abstract: Heterogeneity in treatment efficacy is a major concern in clinical trials. Clustering may help to identify the treatment responders and the non-responders. In the context of longitudinal cluster analyses, sample size and variability of the times of measurements are the main issues with the current methods. Here, we propose a new two-step method for the Clustering of Longitudinal data by using an Extended Baseline. The first step relies on a piecewise linear mixed model for repeated measurements with a treatment-time interaction. The second step clusters the random predictions and considers several parametric (model-based) and non-parametric (partitioning, ascendant hierarchical clustering) algorithms. A simulation study compares all options of the clustering of longitudinal data by using an extended baseline method with the latent-class mixed model. The clustering of longitudinal data by using an extended baseline method with the two model-based algorithms was the more robust model. The clustering of longitudinal data by using an extended baseline method with all the non-parametric algorithms failed when there were unequal variances of treatment effect between clusters or when the subgroups had unbalanced sample sizes. The latent-class mixed model failed when the between-patients slope variability is high. Two real data sets on neurodegenerative disease and on obesity illustrate the clustering of longitudinal data by using an extended baseline method and show how clustering may help to identify the marker(s) of the treatment response. The application of the clustering of longitudinal data by using an extended baseline method in exploratory analysis as the first stage before setting up stratified designs can provide a better estimation of treatment effect in future clinical trials.
authors.affiliation: 1 INSERM UMRS1138, Centre de Recherche des Cordeliers, E22, Université Paris Descartes, Université Pierre et Marie Curie, Paris, France.|2 INSERM U955 E01, Neuropsychologie interventionnelle Laboratory IMRB, Créteil, France.|3 Université Pierre et Marie Curie, Paris 6, Paris, France.|4 École Normale Supérieure, Institut d'Études de la Cognition, Paris, France.
authors.forename: Catherine
authors.initials: C
authors.lastname: Schramm
authors.affiliation: 5 Université de Lyon, CNRS UMR 5208, Polytech Lyon-Université de Lyon 1, Institut Camille Jordan, Villeurbanne, France.
authors.forename: Céline
authors.initials: C
authors.lastname: Vial
authors.affiliation: 2 INSERM U955 E01, Neuropsychologie interventionnelle Laboratory IMRB, Créteil, France.|4 École Normale Supérieure, Institut d'Études de la Cognition, Paris, France.|6 Assistance Publique-Hôpitaux de Paris, National Reference Center for Huntington's Disease Henri Mondor Hospital, Créteil, France.
authors.forename: Anne-Catherine
authors.initials: AC
authors.lastname: Bachoud-Lévi
authors.affiliation: 1 INSERM UMRS1138, Centre de Recherche des Cordeliers, E22, Université Paris Descartes, Université Pierre et Marie Curie, Paris, France.|7 Assistance Publique-Hôpitaux de Paris, Service d'informatique et statistiques, Georges Pompidou European Hospital, Paris, France.
authors.forename: Sandrine
authors.initials: S
authors.lastname: Katsahian
mesh_terms: D000328:Adult; D000465:Algorithms; D014150:Antipsychotic Agents / Q000009:adverse effects; D015992:Body Mass Index; D016000:Cluster Analysis; D005260:Female; D006801:Humans; D006816:Huntington Disease / Q000601:surgery; D008137:Longitudinal Studies; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D009765:Obesity; D057285:Precision Medicine; D016896:Treatment Outcome
chemical_list: D014150:Antipsychotic Agents
keywords: Clustering; Huntington’s disease; longitudinal data; obesity; personalized medicine; treatment effect