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Health Economics Review
Published in: Health Economics Review 1/2015

Open Access 01-12-2015 | Research

Statistical models for the analysis of skewed healthcare cost data: a simulation study

Authors: Amal Saki Malehi, Fatemeh Pourmotahari, Kambiz Ahmadi Angali

Published in: Health Economics Review | Issue 1/2015

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Abstract

Skewed data is the main issue in statistical models in healthcare costs. Data transformation is a conventional method to decrease skewness, but there are some disadvantages. Some recent studies have employed generalized linear models (GLMs) and Cox proportional hazard regression as alternative estimators.
The aim of this study was to investigate how well these alternative estimators perform in terms of bias and precision when the data are skewed. The primary outcome was an estimation of population means of healthcare costs and the secondary outcome was the impact of a covariate on healthcare cost. Alternative estimators, such as ordinary least squares (OLS) for Ln(y) or Log(y), Gamma, Weibull and Cox proportional hazard regression models, were compared using Monte Carlo simulation under different situations, which were generated from skewed distributions.
We found that there was not one best model across all generated conditions. However, GLMs, especially the Gamma regression model, behaved well in the estimation of population means of healthcare costs. The results showed that the Cox proportional hazard model exhibited a poor estimation of population means of healthcare costs and the β1 even under proportional hazard data. Approximately results are consistent by increasing the sample size. However, increasing the sample size could improve the performance of the OLS-based model.
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Metadata
Title
Statistical models for the analysis of skewed healthcare cost data: a simulation study
Authors
Amal Saki Malehi
Fatemeh Pourmotahari
Kambiz Ahmadi Angali
Publication date
01-12-2015
Publisher
Springer Berlin Heidelberg
Published in
Health Economics Review / Issue 1/2015
Electronic ISSN: 2191-1991
DOI
https://doi.org/10.1186/s13561-015-0045-7

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