Literature DB >> 20719587

Validation of a modelling approach for estimating the likely effectiveness of cancer screening using cancer data on prevalence screening and incidence.

Nora Pashayan1, Paul Pharoah, László Tabár, David E Neal, Richard M Martin, Jenny Donovan, Freddie Hamdy, Stephen W Duffy.   

Abstract

PURPOSE: This study aims to validate a biostatistical approach to predict the likely effectiveness of screening in reducing advanced disease in the absence of data on incident screen and interval cancers.
METHODS: We derived the predicted relative reduction in advanced stage disease following screening from the expected proportion of advanced disease following screening and the observed proportion of advanced disease detected clinically among the controls. We compared the predicted estimates to those observed in a randomised trial.
RESULTS: Using our method, the predicted estimates of relative reduction in node positive breast cancer following screening were comparable to the observed estimates for the age groups 50-59 and 60-69 in the screening study (predicted 32% vs. observed 40% (p=0.274) and predicted 34% vs. observed 45% (p=0.068), respectively). However, for the age groups 40-49 and 70-74 the predicted values were overestimates of the likely effectiveness of screening compared to the observed values (predicted 38% vs. observed 16% (p=0.014) and predicted 34% vs. observed 0% (p=0.001), respectively).
CONCLUSION: When the number of cancer cases is more than hundred, the method of prediction using only prevalence screen data may be accurate. Where cancers are less common, for example in small populations or young age groups, further data from interval cancers or incidence screens may be necessary.
Copyright © 2010 Elsevier Ltd. All rights reserved.

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Year:  2010        PMID: 20719587      PMCID: PMC3110612          DOI: 10.1016/j.canep.2010.07.012

Source DB:  PubMed          Journal:  Cancer Epidemiol        ISSN: 1877-7821            Impact factor:   2.984


  18 in total

1.  Progression rates of colorectal cancer by Dukes' stage in a high-risk group: analysis of selective colorectal cancer screening.

Authors:  Jau-Min Wong; Ming-Fang Yen; Mei-Shu Lai; Stephen W Duffy; Robert A Smith; Tony Hsiu-Hsi Chen
Journal:  Cancer J       Date:  2004 May-Jun       Impact factor: 3.360

2.  Modelling the analysis of breast cancer screening programmes: sensitivity, lead time and predictive value in the Florence District Programme (1975-1986).

Authors:  E Paci; S W Duffy
Journal:  Int J Epidemiol       Date:  1991-12       Impact factor: 7.196

3.  Simplified models of screening for chronic disease: estimation procedures from mass screening programmes.

Authors:  N E Day; S D Walter
Journal:  Biometrics       Date:  1984-03       Impact factor: 2.571

4.  Estimation of sojourn time in chronic disease screening without data on interval cases.

Authors:  T H Chen; H S Kuo; M F Yen; M S Lai; L Tabar; S W Duffy
Journal:  Biometrics       Date:  2000-03       Impact factor: 2.571

5.  Markov models of breast tumor progression: some age-specific results.

Authors:  S W Duffy; N E Day; L Tabár; H H Chen; T C Smith
Journal:  J Natl Cancer Inst Monogr       Date:  1997

6.  Overdiagnosis in screening: is the increase in breast cancer incidence rates a cause for concern?

Authors:  E Paci; J Warwick; P Falini; S W Duffy
Journal:  J Med Screen       Date:  2004       Impact factor: 2.136

7.  Update of the Swedish two-county program of mammographic screening for breast cancer.

Authors:  L Tabàr; G Fagerberg; S W Duffy; N E Day; A Gad; O Gröntoft
Journal:  Radiol Clin North Am       Date:  1992-01       Impact factor: 2.303

8.  Reduction in mortality from breast cancer after mass screening with mammography. Randomised trial from the Breast Cancer Screening Working Group of the Swedish National Board of Health and Welfare.

Authors:  L Tabár; C J Fagerberg; A Gad; L Baldetorp; L H Holmberg; O Gröntoft; U Ljungquist; B Lundström; J C Månson; G Eklund
Journal:  Lancet       Date:  1985-04-13       Impact factor: 79.321

9.  Estimation of mean sojourn time in breast cancer screening using a Markov chain model of both entry to and exit from the preclinical detectable phase.

Authors:  S W Duffy; H H Chen; L Tabar; N E Day
Journal:  Stat Med       Date:  1995-07-30       Impact factor: 2.373

10.  Mean sojourn time, overdiagnosis, and reduction in advanced stage prostate cancer due to screening with PSA: implications of sojourn time on screening.

Authors:  N Pashayan; S W Duffy; P Pharoah; D Greenberg; J Donovan; R M Martin; F Hamdy; D E Neal
Journal:  Br J Cancer       Date:  2009-03-17       Impact factor: 7.640

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  3 in total

1.  The psychological impact and experience of breast cancer screening in young women with an increased risk of breast cancer due to neurofibromatosis type 1.

Authors:  Ashley Crook; Rebekah Kwa; Sarah Ephraums; Mathilda Wilding; Lavvina Thiyagarajan; Jane Fleming; Katrina Moore; Yemima Berman
Journal:  Fam Cancer       Date:  2021-05-08       Impact factor: 2.446

2.  Grand challenges in cancer epidemiology and prevention.

Authors:  Farhad Islami; Farin Kamangar; Paolo Boffetta
Journal:  Front Oncol       Date:  2011-04-27       Impact factor: 6.244

3.  The PROFILE Feasibility Study: Targeted Screening of Men With a Family History of Prostate Cancer.

Authors:  Elena Castro; Christos Mikropoulos; Elizabeth K Bancroft; Tokhir Dadaev; Chee Goh; Natalie Taylor; Edward Saunders; Nigel Borley; Diana Keating; Elizabeth C Page; Sibel Saya; Stephen Hazell; Naomi Livni; Nandita deSouza; David Neal; Freddie C Hamdy; Pardeep Kumar; Antonis C Antoniou; Zsofia Kote-Jarai; Rosalind A Eeles
Journal:  Oncologist       Date:  2016-05-05
  3 in total

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