Modelling the distribution of health-related quality of life of advanced melanoma patients in a longitudinal multi-centre clinical trial using M-quantile random effects regression. (February 2018)

• Record Type: Journal Article
• Authors: Borgoni, Riccardo; Del Bianco, Paola; Salvati, Nicola; Schmid, Timo; Tzavidis, Nikos
• Is Part Of: Statistical methods in medical research. Volume 27:Number 2(2018)
• Page Start: 549
• Page End: 563
• Publication Date: 2018-02
• Subjects: Hierarchical data -- quantile regression -- robust estimation -- Rotterdam Symptom Checklist -- Phase III study; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280216636651
• Languages: English
• ISSNs: 0962-2802

Abstract

Health-related quality of life assessment is important in the clinical evaluation of patients with metastatic disease that may offer useful information in understanding the clinical effectiveness of a treatment. To assess if a set of explicative variables impacts on the health-related quality of life, regression models are routinely adopted. However, the interest of researchers may be focussed on modelling other parts (e.g. quantiles) of this conditional distribution. In this paper, we present an approach based on quantile and M-quantile regression to achieve this goal. We applied the methodologies to a prospective, randomized, multi-centre clinical trial. In order to take into account the hierarchical nature of the data we extended the M-quantile regression model to a three-level random effects specification and estimated it by maximum likelihood.