Gaussian Copula–based Regression Models for the Analysis of Mixed Outcomes: An Application on Household's Utilization of Health Services Data

DOI

10.2991/jsta.d.190306.009How to use a DOI?

Keywords

Copula models; mixed outcomes; sampling weights; marginal model

Abstract

In analyzing most correlated outcomes, the popular multivariate Gaussian distribution is very restrictive and therefore dependence modeling using copulas is nowadays very common to take into account the association among mixed outcomes. In this paper, we use Gaussian copula to construct a joint distribution for three mixed discrete and continuous responses. Our approach entails specifying marginal regression models for the outcomes, and combining them via a copula to form a joint model. Closed form for likelihood function is obtained by considering sampling weights. We also obtain the likelihood function for mixed responses where one of the responses, time to event outcome, may have censored values. Some simulation studies are performed to illustrate the performance of the model. Finally, the model is applied on data involving trivariate mixed outcomes on hospitalization of individuals, based on the survey of household's utilization of health services.

Copyright

© 2019 The Authors. Published by Atlantis Press SARL.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Z. Rezaei Ghahroodi
AU  - R. Aliakbari Saba
AU  - T. Baghfalaki
PY  - 2019
DA  - 2019/07/11
TI  - Gaussian Copula–based Regression Models for the Analysis of Mixed Outcomes: An Application on Household's Utilization of Health Services Data
JO  - Journal of Statistical Theory and Applications
SP  - 182
EP  - 197
VL  - 18
IS  - 3
SN  - 2214-1766
UR  - https://doi.org/10.2991/jsta.d.190306.009
DO  - 10.2991/jsta.d.190306.009
ID  - Ghahroodi2019
ER  -