Abstract
Accurate estimation and prediction of healthcare costs play crucial roles in decisions made by healthcare agencies on policy and resource allocation. Development of a cost model allows these decision-makers the opportunity to investigate the impact of different policies and/or allocations of resources. With increased subject-specific information, longitudinal studies and the breakdown of total costs into categories comes the need for healthcare cost models to account for correlation. In this article, we review the statistical models used to fit joint costs, emphasizing the use of copulas as a flexible and relatively straightforward approach to move from marginal to joint modeling.
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