Literature DB >> 2384271

A model-based analysis of the HIP project for breast cancer screening.

G J van Oortmarssen1, J D Habbema, J T Lubbe, P J van der Maas.   

Abstract

A computer simulation approach is used to test assumptions about sensitivity of mammography and physical examination, and about the duration of preclinical screen-detectable breast cancer. Values between 50% and 80% for the combined sensitivity of the 2 tests give a good explanation of the results of the HIP randomized trial of breast cancer screening. The mean duration of the preclinical stage can vary from 1.6 years for high sensitivity values to 2.7 years for low values. In comparison with previous analyses of the HIP data our estimate for the sensitivity is lower, and the mean duration of the preclinical stage is longer. This is a consequence of the use of a more detailed model in our analysis, allowing for a more complete use of the HIP data in testing model assumptions. Similar analyses of data from recent screening projects in The Netherlands resulted in compatible estimates for the duration of preclinical breast cancer.

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Year:  1990        PMID: 2384271     DOI: 10.1002/ijc.2910460211

Source DB:  PubMed          Journal:  Int J Cancer        ISSN: 0020-7136            Impact factor:   7.396


  8 in total

Review 1.  Calibration methods used in cancer simulation models and suggested reporting guidelines.

Authors:  Natasha K Stout; Amy B Knudsen; Chung Yin Kong; Pamela M McMahon; G Scott Gazelle
Journal:  Pharmacoeconomics       Date:  2009       Impact factor: 4.981

2.  An analysis of the efficacy of serial screening for familial nasopharyngeal carcinoma based on Markov chain models.

Authors:  Cheuk Wai Choi; Michael C H Lee; Wai Tong Ng; Lai Yau Law; Tsz Kok Yau; Anne W M Lee
Journal:  Fam Cancer       Date:  2011-03       Impact factor: 2.375

3.  Age-specific sensitivities of mammographic screening for breast cancer.

Authors:  P G Peer; A L Verbeek; H Straatman; J H Hendriks; R Holland
Journal:  Breast Cancer Res Treat       Date:  1996       Impact factor: 4.872

4.  Identifiability of the joint distribution of age and tumor size at detection in the presence of screening.

Authors:  Leonid Hanin; Andrei Yakovlev
Journal:  Math Biosci       Date:  2007-01-12       Impact factor: 2.144

5.  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

6.  Individually tailored screening of breast cancer with genes, tumour phenotypes, clinical attributes, and conventional risk factors.

Authors:  Y-Y Wu; M-F Yen; C-P Yu; H-H Chen
Journal:  Br J Cancer       Date:  2013-05-14       Impact factor: 7.640

7.  Estimation of progression of multi-state chronic disease using the Markov model and prevalence pool concept.

Authors:  Hui-Chuan Shih; Pesus Chou; Chi-Ming Liu; Tao-Hsin Tung
Journal:  BMC Med Inform Decis Mak       Date:  2007-11-09       Impact factor: 2.796

8.  Changes in use of breast-conserving therapy in years 1978-2000.

Authors:  H J de Koning; J A van Dongen; P J van der Maas
Journal:  Br J Cancer       Date:  1994-12       Impact factor: 7.640

  8 in total

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