Parameter heterogeneity in breast cancer cost regressions - Evidence from five European countries

Joel Smith*, Helen Banks, Harry Campbell, Anne Douglas, Eilidh Fletcher, Alison McCallum, Tron Anders Moger, Mikko Peltola, Sofia Sveréus, Sarah Wild, Linda J. Williams, John Forbes

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

We investigate parameter heterogeneity in breast cancer 1-year cumulative hospital costs across five European countries as part of the EuroHOPE project. The paper aims to explore whether conditional mean effects provide a suitable representation of the national variation in hospital costs. A cohort of patients with a primary diagnosis of invasive breast cancer (ICD-9 codes 174 and ICD-10 C50 codes) is derived using routinely collected individual breast cancer data from Finland, the metropolitan area of Turin (Italy), Norway, Scotland and Sweden. Conditional mean effects are estimated by ordinary least squares for each country, and quantile regressions are used to explore heterogeneity across the conditional quantile distribution. Point estimates based on conditional mean effects provide a good approximation of treatment response for some key demographic and diagnostic specific variables (e.g. age and ICD-10 diagnosis) across the conditional quantile distribution. For many policy variables of interest, however, there is considerable evidence of parameter heterogeneity that is concealed if decisions are based solely on conditional mean results. The use of quantile regression methods reinforce the need to consider beyond an average effect given the greater recognition that breast cancer is a complex disease reflecting patient heterogeneity.

Original languageEnglish
Pages (from-to)23-37
Number of pages15
JournalHealth Economics
Publication statusPublished - 3 Dec 2015

Keywords / Materials (for Non-textual outputs)

  • breast cancer
  • cost distribution
  • parameter heterogeneity
  • quantile regression


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