Literature DB >> 29402176

Estimating the frequency of indolent breast cancer in screening trials.

Yu Shen1, Wenli Dong1, Roman Gulati2, Marc D Ryser3,4, Ruth Etzioni2.   

Abstract

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.

Entities:  

Keywords:  Breast cancer; indolent cancer; mammography screening; maximum likelihood estimation; overdiagnosis; randomized controlled trials

Mesh:

Year:  2018        PMID: 29402176      PMCID: PMC6027608          DOI: 10.1177/0962280217754232

Source DB:  PubMed          Journal:  Stat Methods Med Res        ISSN: 0962-2802            Impact factor:   3.021


  26 in total

1.  Quantifying the potential problem of overdiagnosis of ductal carcinoma in situ in breast cancer screening.

Authors:  M-F Yen; L Tabár; B Vitak; R A Smith; H-H Chen; S W Duffy
Journal:  Eur J Cancer       Date:  2003-08       Impact factor: 9.162

2.  Collaborative Modeling of the Benefits and Harms Associated With Different U.S. Breast Cancer Screening Strategies.

Authors:  Jeanne S Mandelblatt; Natasha K Stout; Clyde B Schechter; Jeroen J van den Broek; Diana L Miglioretti; Martin Krapcho; Amy Trentham-Dietz; Diego Munoz; Sandra J Lee; Donald A Berry; Nicolien T van Ravesteyn; Oguzhan Alagoz; Karla Kerlikowske; Anna N A Tosteson; Aimee M Near; Amanda Hoeffken; Yaojen Chang; Eveline A Heijnsdijk; Gary Chisholm; Xuelin Huang; Hui Huang; Mehmet Ali Ergun; Ronald Gangnon; Brian L Sprague; Sylvia Plevritis; Eric Feuer; Harry J de Koning; Kathleen A Cronin
Journal:  Ann Intern Med       Date:  2016-01-12       Impact factor: 25.391

3.  Robust modeling in screening studies: estimation of sensitivity and preclinical sojourn time distribution.

Authors:  Yu Shen; Marvin Zelen
Journal:  Biostatistics       Date:  2005-04-28       Impact factor: 5.899

4.  Overdiagnosis of invasive breast cancer due to mammography screening: results from the Norwegian screening program.

Authors:  Mette Kalager; Hans-Olov Adami; Michael Bretthauer; Rulla M Tamimi
Journal:  Ann Intern Med       Date:  2012-04-03       Impact factor: 25.391

5.  Evidence on screening for breast cancer from a randomized trial.

Authors:  S Shapiro
Journal:  Cancer       Date:  1977-06       Impact factor: 6.860

6.  Overdiagnosis in breast cancer: design and methods of estimation in observational studies.

Authors:  Donella Puliti; Guido Miccinesi; Eugenio Paci
Journal:  Prev Med       Date:  2011-06-02       Impact factor: 4.018

7.  Canadian National Breast Screening Study: 1. Breast cancer detection and death rates among women aged 40 to 49 years.

Authors:  A B Miller; C J Baines; T To; C Wall
Journal:  CMAJ       Date:  1992-11-15       Impact factor: 8.262

Review 8.  Lead-time models should not be used to estimate overdiagnosis in cancer screening.

Authors:  Per-Henrik Zahl; Karsten Juhl Jørgensen; Peter C Gøtzsche
Journal:  J Gen Intern Med       Date:  2014-03-04       Impact factor: 5.128

9.  Overdiagnosis from non-progressive cancer detected by screening mammography: stochastic simulation study with calibration to population based registry data.

Authors:  Arnaud Seigneurin; Olivier François; José Labarère; Pierre Oudeville; Jean Monlong; Marc Colonna
Journal:  BMJ       Date:  2011-11-23

10.  Overdiagnosis and overtreatment of breast cancer: estimates of overdiagnosis from two trials of mammographic screening for breast cancer.

Authors:  Stephen W Duffy; Olorunsola Agbaje; Laszlo Tabar; Bedrich Vitak; Nils Bjurstam; Lena Björneld; Jonathan P Myles; Jane Warwick
Journal:  Breast Cancer Res       Date:  2005-11-10       Impact factor: 6.466

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  3 in total

1.  A progressive three-state model to estimate time to cancer: a likelihood-based approach.

Authors:  Eddymurphy U Akwiwu; Thomas Klausch; Henriette C Jodal; Beatriz Carvalho; Magnus Løberg; Mette Kalager; Johannes Berkhof; Veerle M H Coupé
Journal:  BMC Med Res Methodol       Date:  2022-06-27       Impact factor: 4.612

Review 2.  The future of early cancer detection.

Authors:  Rebecca C Fitzgerald; Antonis C Antoniou; Ljiljana Fruk; Nitzan Rosenfeld
Journal:  Nat Med       Date:  2022-04-19       Impact factor: 87.241

3.  Quantifying the duration of the preclinical detectable phase in cancer screening: a systematic review.

Authors:  Sandra M E Geurts; Anne M W M Aarts; André L M Verbeek; Tony H H Chen; Mireille J M Broeders; Stephen W Duffy
Journal:  Epidemiol Health       Date:  2022-01-03
  3 in total

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