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
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
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.
Hierarchical data, quantile regression
Borgoni, Riccardo
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Del Bianco, P.
accc5bb6-e472-4f57-ab44-dbea39e6dd2f
Salvati, Nicola
9be298e5-de55-4a24-9361-054a2ec09726
Schmid, Timo
6337d53e-bfc0-4a18-b31c-551d2f859336
Tzavidis, Nikolaos
431ec55d-c147-466d-9c65-0f377b0c1f6a
17 March 2016
Borgoni, Riccardo
df9c90ab-c2d2-47d6-bcc7-1444a605d6ff
Del Bianco, P.
accc5bb6-e472-4f57-ab44-dbea39e6dd2f
Salvati, Nicola
9be298e5-de55-4a24-9361-054a2ec09726
Schmid, Timo
6337d53e-bfc0-4a18-b31c-551d2f859336
Tzavidis, Nikolaos
431ec55d-c147-466d-9c65-0f377b0c1f6a
Borgoni, Riccardo, Del Bianco, P., Salvati, Nicola, Schmid, Timo and Tzavidis, Nikolaos
(2016)
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.
Statistical Methods in Medical Research.
(doi:10.1177/0962280216636651).
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.
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Published date: 17 March 2016
Keywords:
Hierarchical data, quantile regression
Organisations:
Social Statistics & Demography
Identifiers
Local EPrints ID: 409681
URI: http://eprints.soton.ac.uk/id/eprint/409681
ISSN: 0962-2802
PURE UUID: a4af1268-3ae2-4b23-afbc-6716a46b2277
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Date deposited: 01 Jun 2017 04:05
Last modified: 16 Mar 2024 03:23
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Contributors
Author:
Riccardo Borgoni
Author:
P. Del Bianco
Author:
Nicola Salvati
Author:
Timo Schmid
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