Identification of the Fraction of Indolent Tumors and Associated Overdiagnosis in Breast Cancer Screening Trials.


Journal

American journal of epidemiology
ISSN: 1476-6256
Titre abrégé: Am J Epidemiol
Pays: United States
ID NLM: 7910653

Informations de publication

Date de publication:
01 01 2019
Historique:
received: 21 10 2017
accepted: 14 09 2018
pubmed: 17 10 2018
medline: 19 11 2019
entrez: 17 10 2018
Statut: ppublish

Résumé

It is generally accepted that some screen-detected breast cancers are overdiagnosed and would not progress to symptomatic cancer if left untreated. However, precise estimates of the fraction of nonprogressive cancers remain elusive. In recognition of the weaknesses of overdiagnosis estimation methods based on excess incidence, there is a need for model-based approaches that accommodate nonprogressive lesions. Here, we present an in-depth analysis of a generalized model of breast cancer natural history that allows for a mixture of progressive and indolent lesions. We provide a formal proof of global structural identifiability of the model and use simulation to identify conditions that allow for parameter estimates that are sufficiently precise and practically actionable. We show that clinical follow-up after the last screening can play a critical role in ensuring adequately precise identification of the fraction of indolent cancers in a stop-screen trial design, and we demonstrate that model misspecification can lead to substantially biased estimates of mean sojourn time. Finally, we illustrate our findings using the example of Canadian National Breast Screening Study 2 (1980-1985) and show that the fraction of indolent cancers is not precisely identifiable. Our findings provide the foundation for extended models that account for both in situ and invasive lesions.

Identifiants

pubmed: 30325415
pii: 5133242
doi: 10.1093/aje/kwy214
pmc: PMC6321806
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, U.S. Gov't, Non-P.H.S.

Langues

eng

Sous-ensembles de citation

IM

Pagination

197-205

Subventions

Organisme : NCI NIH HHS
ID : U01 CA152958
Pays : United States
Organisme : NCI NIH HHS
ID : R01 CA192402
Pays : United States
Organisme : NCI NIH HHS
ID : P30 CA016672
Pays : United States
Organisme : NCI NIH HHS
ID : R50 CA221836
Pays : United States
Organisme : NCI NIH HHS
ID : U01 CA199218
Pays : United States
Organisme : NCI NIH HHS
ID : U01 CA182915
Pays : United States
Organisme : NCI NIH HHS
ID : U01 CA157224
Pays : United States
Organisme : NCI NIH HHS
ID : K99 CA207872
Pays : United States
Organisme : NCI NIH HHS
ID : R00 CA207872
Pays : United States

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Auteurs

Marc D Ryser (MD)

Department of Surgery, Duke University Medical Center, Durham, North Carolina.
Department of Mathematics, Trinity College of Arts and Sciences, Duke University, Durham, North Carolina.

Roman Gulati (R)

Program in Biostatistics, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington.

Marisa C Eisenberg (MC)

Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan.

Yu Shen (Y)

Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, Texas.

E Shelley Hwang (ES)

Department of Surgery, Duke University Medical Center, Durham, North Carolina.

Ruth B Etzioni (RB)

Program in Biostatistics, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington.

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