Joint models of tumour size and lymph node spread for incident breast cancer cases in the presence of screening.


Journal

Statistical methods in medical research
ISSN: 1477-0334
Titre abrégé: Stat Methods Med Res
Pays: England
ID NLM: 9212457

Informations de publication

Date de publication:
12 2019
Historique:
pubmed: 5 1 2019
medline: 29 12 2020
entrez: 5 1 2019
Statut: ppublish

Résumé

Continuous growth models show great potential for analysing cancer screening data. We recently described such a model for studying breast cancer tumour growth based on modelling tumour size at diagnosis, as a function of screening history, detection mode, and relevant patient characteristics. In this article, we describe how the approach can be extended to jointly model tumour size and number of lymph node metastases at diagnosis. We propose a new class of lymph node spread models which are biologically motivated and describe how they can be extended to incorporate random effects to allow for heterogeneity in underlying rates of spread. Our final model provides a dramatically better fit to empirical data on 1860 incident breast cancer cases than models in current use. We validate our lymph node spread model on an independent data set consisting of 3961 women diagnosed with invasive breast cancer.

Identifiants

pubmed: 30606087
doi: 10.1177/0962280218819568
pmc: PMC6745622
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

3822-3842

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Auteurs

Gabriel Isheden (G)

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden.

Linda Abrahamsson (L)

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden.

Therese Andersson (T)

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden.

Kamila Czene (K)

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden.

Keith Humphreys (K)

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden.

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Classifications MeSH