Highlighting differences between conditional and unconditional quantile regression approaches through an application to assess medication adherence

Bijan J Borah, Anirban Basu

Research output: Contribution to journalArticle

43 Citations (Scopus)

Abstract

The quantile regression (QR) framework provides a pragmatic approach in understanding the differential impacts of covariates along the distribution of an outcome. However, the QR framework that has pervaded the applied economics literature is based on the conditional quantile regression method. It is used to assess the impact of a covariate on a quantile of the outcome conditional on specific values of other covariates. In most cases, conditional quantile regression may generate results that are often not generalizable or interpretable in a policy or population context. In contrast, the unconditional quantile regression method provides more interpretable results as it marginalizes the effect over the distributions of other covariates in the model. In this paper, the differences between these two regression frameworks are highlighted, both conceptually and econometrically. Additionally, using real-world claims data from a large US health insurer, alternative QR frameworks are implemented to assess the differential impacts of covariates along the distribution of medication adherence among elderly patients with Alzheimer's disease.

Original languageEnglish (US)
Pages (from-to)1052-1070
Number of pages19
JournalHealth Economics (United Kingdom)
Volume22
Issue number9
DOIs
StatePublished - Sep 2013

Fingerprint

Medication Adherence
Insurance Carriers
Public Policy
Alzheimer Disease
Economics
Health

Keywords

  • Alzheimer's disease
  • conditional quantile regression
  • medication adherence
  • medication possession ratio
  • unconditional quantile regression

ASJC Scopus subject areas

  • Health Policy

Cite this

@article{f6cd1b3dc22c48a4a3001d371c9beb98,
title = "Highlighting differences between conditional and unconditional quantile regression approaches through an application to assess medication adherence",
abstract = "The quantile regression (QR) framework provides a pragmatic approach in understanding the differential impacts of covariates along the distribution of an outcome. However, the QR framework that has pervaded the applied economics literature is based on the conditional quantile regression method. It is used to assess the impact of a covariate on a quantile of the outcome conditional on specific values of other covariates. In most cases, conditional quantile regression may generate results that are often not generalizable or interpretable in a policy or population context. In contrast, the unconditional quantile regression method provides more interpretable results as it marginalizes the effect over the distributions of other covariates in the model. In this paper, the differences between these two regression frameworks are highlighted, both conceptually and econometrically. Additionally, using real-world claims data from a large US health insurer, alternative QR frameworks are implemented to assess the differential impacts of covariates along the distribution of medication adherence among elderly patients with Alzheimer's disease.",
keywords = "Alzheimer's disease, conditional quantile regression, medication adherence, medication possession ratio, unconditional quantile regression",
author = "Borah, {Bijan J} and Anirban Basu",
year = "2013",
month = "9",
doi = "10.1002/hec.2927",
language = "English (US)",
volume = "22",
pages = "1052--1070",
journal = "Health Economics (United Kingdom)",
issn = "1057-9230",
publisher = "John Wiley and Sons Ltd",
number = "9",

}

TY - JOUR

T1 - Highlighting differences between conditional and unconditional quantile regression approaches through an application to assess medication adherence

AU - Borah, Bijan J

AU - Basu, Anirban

PY - 2013/9

Y1 - 2013/9

N2 - The quantile regression (QR) framework provides a pragmatic approach in understanding the differential impacts of covariates along the distribution of an outcome. However, the QR framework that has pervaded the applied economics literature is based on the conditional quantile regression method. It is used to assess the impact of a covariate on a quantile of the outcome conditional on specific values of other covariates. In most cases, conditional quantile regression may generate results that are often not generalizable or interpretable in a policy or population context. In contrast, the unconditional quantile regression method provides more interpretable results as it marginalizes the effect over the distributions of other covariates in the model. In this paper, the differences between these two regression frameworks are highlighted, both conceptually and econometrically. Additionally, using real-world claims data from a large US health insurer, alternative QR frameworks are implemented to assess the differential impacts of covariates along the distribution of medication adherence among elderly patients with Alzheimer's disease.

AB - The quantile regression (QR) framework provides a pragmatic approach in understanding the differential impacts of covariates along the distribution of an outcome. However, the QR framework that has pervaded the applied economics literature is based on the conditional quantile regression method. It is used to assess the impact of a covariate on a quantile of the outcome conditional on specific values of other covariates. In most cases, conditional quantile regression may generate results that are often not generalizable or interpretable in a policy or population context. In contrast, the unconditional quantile regression method provides more interpretable results as it marginalizes the effect over the distributions of other covariates in the model. In this paper, the differences between these two regression frameworks are highlighted, both conceptually and econometrically. Additionally, using real-world claims data from a large US health insurer, alternative QR frameworks are implemented to assess the differential impacts of covariates along the distribution of medication adherence among elderly patients with Alzheimer's disease.

KW - Alzheimer's disease

KW - conditional quantile regression

KW - medication adherence

KW - medication possession ratio

KW - unconditional quantile regression

UR - http://www.scopus.com/inward/record.url?scp=84883133990&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84883133990&partnerID=8YFLogxK

U2 - 10.1002/hec.2927

DO - 10.1002/hec.2927

M3 - Article

C2 - 23616446

AN - SCOPUS:84883133990

VL - 22

SP - 1052

EP - 1070

JO - Health Economics (United Kingdom)

JF - Health Economics (United Kingdom)

SN - 1057-9230

IS - 9

ER -