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

Riccardo Borgini, Paola Del Bianco, Nicola Salvati, Timo Schmid and Nikos Tzavidis

No 2015/19, Discussion Papers from Free University Berlin, School of Business & Economics

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 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.

Keywords: hierarchical data; in uence function; robust estimation; quantile regression; multilevel modelling (search for similar items in EconPapers)
Date: 2015
New Economics Papers: this item is included in nep-ecm and nep-hea
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