Evidence map›Paper›PMID 26633866›Full record

ArticleHealth economics2015

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 and 3 more

Abstract read
In one paragraph

Article in Health economics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Trends and drivers of pharmaceutical expenditures from systemic anti-cancer therapy.The European journal of health economics : HEPAC : health economics in prevention and care · 2023
    Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Joel SmithHealth Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
Helen BanksCentre for Research on Health and Social Care Management, CERGAS, Bocconi University.
Harry CampbellUsher Institute of Population Health Sciences and Informatics, The University of Edinburgh, Edinburgh, UK.
Anne DouglasUsher Institute of Population Health Sciences and Informatics, The University of Edinburgh, Edinburgh, UK.
Eilidh FletcherLothian Analytical Services, NHS Lothian, Edinburgh, UK.
Alison McCallumNHS Lothian, Edinburgh, UK.
Tron Anders MogerDepartment of Health Management and Health Economics, Institute of Health and Society, University of Oslo, Oslo, Norway.
Mikko PeltolaCentre for Health and Social Economics (CHESS), National Institute for Health and Welfare, Helsinki, Finland.
Sofia SveréusMedical Management Centre, Karolinska Institutet, Stockholm, Sweden.
Sarah WildUsher Institute of Population Health Sciences and Informatics, The University of Edinburgh, Edinburgh, UK.
Linda J WilliamsUsher Institute of Population Health Sciences and Informatics, The University of Edinburgh, Edinburgh, UK.
John ForbesHealth Research Institute, University of Limerick, Limerick, Ireland.
EuroHOPE study group

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Breast NeoplasmsEuropeFemaleHospital CostsHumansModels, EconometricRegression Analysisbreast cancercost distributionparameter heterogeneityquantile regression

Identifiers

PMID26633866
PMCPMC5063195

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.