Borgini, Riccardo Bianco, Paola Del Salvati, Nicola Schmid, Timo Tzavidis, Nikos
Year of Publication:
Discussion Paper, School of Business & Economics: Economics 2015/19
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 M-quantile regression to achieve this goal. We applied the proposed methodology 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.
hierarchical data in uence function robust estimation quantile regression multilevel modelling