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A Unifying Switching Regime Regression Framework with Applications in Health Economics

Marra, Giampiero; Radice, Rosalba; Zimmer, David; (2022) A Unifying Switching Regime Regression Framework with Applications in Health Economics. UCL Department of Statistical Science: London, UK. Green open access

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Abstract

Motivated by three health economics-related case studies, we propose a unifying and flexible modelling framework in the context of the utility-based Roy model of switching regimes. The proposal can handle the peculiar distributional shapes of the considered outcomes via a vast range of marginal distributions, allows for a wide variety of copula dependence structures and permits to specify all model parameters (including the depen- dence parameters) as flexible functions of covariate effects. The algorithm is based on a computationally efficient and stable penalised maximum likelihood estimation approach with integrated automatic multiple smoothing parameter selection. Inferential results are also readily available. The proposed modelling framework is evaluated using simulated data and employed for three applications in health economics, that use data from the Medical Expenditure Panel Survey, where novel patterns are uncovered. The new frame- work has been incorporated in the R package GJRM, hence allowing any user to fit the desired model(s) and produce easy-to-interpret numerical and visual summaries.

Type: Working / discussion paper
Title: A Unifying Switching Regime Regression Framework with Applications in Health Economics
Open access status: An open access version is available from UCL Discovery
Publisher version: http://www.homepages.ucl.ac.uk/~ucakgm0/draft.pdf
Language: English
Additional information: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: copula; penalised regression spline; simultaneous estimation; structural equation model, switching regime.
UCL classification: UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL
URI: https://discovery.ucl.ac.uk/id/eprint/10146191
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