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

49 Scopus citations

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

Keywords

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

ASJC Scopus subject areas

  • Health Policy

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