Modeling correlated healthcare costs

Joanne Daggy, Joseph Thomas, Bruce A. Craig

Research output: Contribution to journalArticle

7 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)101-111
Number of pages11
JournalExpert Review of Pharmacoeconomics and Outcomes Research
Volume11
Issue number1
DOIs
StatePublished - Feb 2011

Fingerprint

Health Care Costs
Resource Allocation
Costs and Cost Analysis
Joints
Statistical Models
Longitudinal Studies
Delivery of Health Care

Keywords

  • copulas
  • excess zeros
  • healthcare costs
  • heavy-tailed distributions
  • two-part models

ASJC Scopus subject areas

  • Pharmacology (medical)
  • Health Policy

Cite this

Modeling correlated healthcare costs. / Daggy, Joanne; Thomas, Joseph; Craig, Bruce A.

In: Expert Review of Pharmacoeconomics and Outcomes Research, Vol. 11, No. 1, 02.2011, p. 101-111.

Research output: Contribution to journalArticle

Daggy, Joanne ; Thomas, Joseph ; Craig, Bruce A. / Modeling correlated healthcare costs. In: Expert Review of Pharmacoeconomics and Outcomes Research. 2011 ; Vol. 11, No. 1. pp. 101-111.
@article{7220d282c5db43ec947fea50d6b98012,
title = "Modeling correlated healthcare costs",
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.",
keywords = "copulas, excess zeros, healthcare costs, heavy-tailed distributions, two-part models",
author = "Joanne Daggy and Joseph Thomas and Craig, {Bruce A.}",
year = "2011",
month = "2",
doi = "10.1586/erp.10.92",
language = "English",
volume = "11",
pages = "101--111",
journal = "Expert Review of Pharmacoeconomics and Outcomes Research",
issn = "1473-7167",
publisher = "Expert Reviews Ltd.",
number = "1",

}

TY - JOUR

T1 - Modeling correlated healthcare costs

AU - Daggy, Joanne

AU - Thomas, Joseph

AU - Craig, Bruce A.

PY - 2011/2

Y1 - 2011/2

N2 - 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.

AB - 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.

KW - copulas

KW - excess zeros

KW - healthcare costs

KW - heavy-tailed distributions

KW - two-part models

UR - http://www.scopus.com/inward/record.url?scp=79952174237&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79952174237&partnerID=8YFLogxK

U2 - 10.1586/erp.10.92

DO - 10.1586/erp.10.92

M3 - Article

C2 - 21351862

AN - SCOPUS:79952174237

VL - 11

SP - 101

EP - 111

JO - Expert Review of Pharmacoeconomics and Outcomes Research

JF - Expert Review of Pharmacoeconomics and Outcomes Research

SN - 1473-7167

IS - 1

ER -