Literature DB >> 9147591

Estimating lead time and sensitivity in a screening program without estimating the incidence in the screened group.

H Straatman1, P G Peer, A L Verbeek.   

Abstract

Early indicators of the effectiveness of a screening test for chronic diseases such as breast cancer are the length of time the diagnosis is advanced by screening, the lead time, and the sensitivity of the screening test. This paper describes a model for simultaneously estimating the mean lead time and the sensitivity when only the number of cancers detected at the successive screenings and the number of cancers occurring in the time interval between the screening examinations are known. This model is particularly useful in assessing the effect of screening when the underlying cancer incidence in the screened group is unknown. The model is fitted to the data of 235 screen-detected breast cancer cases and 146 interval cancers diagnosed across 6 screening rounds of the program in Nijmegen. The maximum likelihood estimate for the mean lead time ranges from 1.3 years in the under age 50 group to 2.2 years in the age 50-65 group, both estimates having large confidence intervals. The corresponding sensitivity estimates are 0.92 and 1.00.

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Mesh:

Year:  1997        PMID: 9147591

Source DB:  PubMed          Journal:  Biometrics        ISSN: 0006-341X            Impact factor:   2.571


  10 in total

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2.  Simulation Procedure in Periodic Cancer Screening Trials.

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5.  Bayesian inference for the lead time in periodic cancer screening.

Authors:  Dongfeng Wu; Gary L Rosner; Lyle D Broemeling
Journal:  Biometrics       Date:  2007-09       Impact factor: 2.571

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

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Journal:  Epidemiol Health       Date:  2022-01-03

8.  Inferences for the Lead Time in Breast Cancer Screening Trials under a Stable Disease Model.

Authors:  Justin Shows; Dongfeng Wu
Journal:  Cancers (Basel)       Date:  2011-04-26       Impact factor: 6.639

9.  Quantifying the natural history of breast cancer.

Authors:  K H X Tan; L Simonella; H L Wee; A Roellin; Y-W Lim; W-Y Lim; K S Chia; M Hartman; A R Cook
Journal:  Br J Cancer       Date:  2013-10-01       Impact factor: 7.640

10.  Bayesian lead time estimation for the Johns Hopkins Lung Project data.

Authors:  Hyejeong Jang; Seongho Kim; Dongfeng Wu
Journal:  J Epidemiol Glob Health       Date:  2013-06-14
  10 in total

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