Estimating the frequency of indolent breast cancer in screening trials.


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:
04 2019
Historique:
pubmed: 7 2 2018
medline: 10 7 2020
entrez: 7 2 2018
Statut: ppublish

Résumé

Cancer screening can detect cancer that would not have been detected in a patient's lifetime without screening. Standard methods for analyzing screening data do not explicitly account for the possibility that a fraction of tumors may remain latent indefinitely. We extend these methods by representing cancers as a mixture of those that progress to symptoms (progressive) and those that remain latent (indolent). Given sensitivity of the screening test, we derive likelihood expressions to simultaneously estimate (1) the rate of onset of preclinical cancer, (2) the average preclinical duration of progressive cancers, and (3) the fraction of preclinical cancers that are indolent. Simulations demonstrate satisfactory performance of the estimation approach to identify model parameters subject to precise specifications of input parameters and adequate numbers of interval cancers. In application to four breast cancer screening trials, the estimated indolent fraction among preclinical cancers varies between 2% and 35% when assuming 80% test sensitivity and varying specifications for the earliest time that participants could plausibly have developed cancer. We conclude that standard methods for analyzing screening data can be extended to allow some indolent cancers, but accurate estimation depends on correctly specifying key inputs that may be difficult to determine precisely in practice.

Identifiants

pubmed: 29402176
doi: 10.1177/0962280217754232
pmc: PMC6027608
mid: NIHMS951386
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural

Langues

eng

Sous-ensembles de citation

IM

Pagination

1261-1271

Subventions

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 : K99 CA207872
Pays : United States
Organisme : NCI NIH HHS
ID : R00 CA207872
Pays : United States

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Auteurs

Yu Shen (Y)

1 Department of Biostatistics, MD Anderson Cancer Center, Houston, TX, USA.

Wenli Dong (W)

1 Department of Biostatistics, MD Anderson Cancer Center, Houston, TX, USA.

Roman Gulati (R)

2 Program in Biostatistics, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.

Marc D Ryser (MD)

3 Department of Surgery, Division of Advanced Oncologic and GI Surgery, Duke University Medical Center, Durham, NC, USA.
4 Department of Mathematics, Duke University, Durham, NC, USA.

Ruth Etzioni (R)

2 Program in Biostatistics, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.

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Classifications MeSH