Literature DB >> 33939839

Nonhomogeneous Markov chain for estimating the cumulative risk of multiple false positive screening tests.

Marzieh K Golmakani1, Rebecca A Hubbard2, Diana L Miglioretti3.   

Abstract

Screening tests are widely recommended for the early detection of disease among asymptomatic individuals. While detecting disease at an earlier stage has the potential to improve outcomes, screening also has negative consequences, including false positive results which may lead to anxiety, unnecessary diagnostic procedures, and increased healthcare costs. In addition, multiple false positive results could discourage participating in subsequent screening rounds. Screening guidelines typically recommend repeated screening over a period of many years, but little prior research has investigated how often individuals receive multiple false positive test results. Estimating the cumulative risk of multiple false positive results over the course of multiple rounds of screening is challenging due to the presence of censoring and competing risks, which may depend on the false positive risk, screening round, and number of prior false positive results. To address the general challenge of estimating the cumulative risk of multiple false positive test results, we propose a nonhomogeneous multistate model to describe the screening process including competing events. We developed alternative approaches for estimating the cumulative risk of multiple false positive results using this multistate model based on existing estimators for the cumulative risk of a single false positive. We compared the performance of the newly proposed models through simulation studies and illustrate model performance using data on screening mammography from the Breast Cancer Surveillance Consortium. Across most simulation scenarios, the multistate extension of a censoring bias model demonstrated lower bias compared to other approaches. In the context of screening mammography, we found that the cumulative risk of multiple false positive results is high. For instance, based on the censoring bias model, for a high-risk individual, the cumulative probability of at least two false positive mammography results after 10 rounds of annual screening is 40.4.
© 2021 The International Biometric Society.

Entities:  

Keywords:  Breast Cancer Surveillance Consortium; censoring; competing risk; mammography; multistate model; stochastic model

Mesh:

Year:  2021        PMID: 33939839      PMCID: PMC8793126          DOI: 10.1111/biom.13484

Source DB:  PubMed          Journal:  Biometrics        ISSN: 0006-341X            Impact factor:   1.701


  10 in total

1.  Inference in randomized studies with informative censoring and discrete time-to-event endpoints.

Authors:  D Scharfstein; J M Robins; W Eddings; A Rotnitzky
Journal:  Biometrics       Date:  2001-06       Impact factor: 2.571

2.  Radiation-Induced Breast Cancer Incidence and Mortality From Digital Mammography Screening: A Modeling Study.

Authors:  Diana L Miglioretti; Jane Lange; Jeroen J van den Broek; Christoph I Lee; Nicolien T van Ravesteyn; Dominique Ritley; Karla Kerlikowske; Joshua J Fenton; Joy Melnikow; Harry J de Koning; Rebecca A Hubbard
Journal:  Ann Intern Med       Date:  2016-01-12       Impact factor: 25.391

3.  Re-attendance at biennial screening mammography following a repeated false positive recall.

Authors:  Elisabeth G Klompenhouwer; Lucien E M Duijm; Adri C Voogd; Gerard J den Heeten; Luc J Strobbe; Marieke W Louwman; Jan Willem Coebergh; Dick Venderink; Mireille J M Broeders
Journal:  Breast Cancer Res Treat       Date:  2014-04-19       Impact factor: 4.872

4.  Effect of false-positive mammograms on interval breast cancer screening in a health maintenance organization.

Authors:  M L Burman; S H Taplin; D F Herta; J G Elmore
Journal:  Ann Intern Med       Date:  1999-07-06       Impact factor: 25.391

5.  Modelling the cumulative risk of a false-positive screening test.

Authors:  Rebecca A Hubbard; Diana L Miglioretti; Robert A Smith
Journal:  Stat Methods Med Res       Date:  2010-03-31       Impact factor: 3.021

6.  Modelling the cumulative risk for a false-positive under repeated screening events.

Authors:  A E Gelfand; F Wang
Journal:  Stat Med       Date:  2000-07-30       Impact factor: 2.373

7.  National Performance Benchmarks for Modern Screening Digital Mammography: Update from the Breast Cancer Surveillance Consortium.

Authors:  Constance D Lehman; Robert F Arao; Brian L Sprague; Janie M Lee; Diana S M Buist; Karla Kerlikowske; Louise M Henderson; Tracy Onega; Anna N A Tosteson; Garth H Rauscher; Diana L Miglioretti
Journal:  Radiology       Date:  2016-12-05       Impact factor: 11.105

8.  A semiparametric censoring bias model for estimating the cumulative risk of a false-positive screening test under dependent censoring.

Authors:  Rebecca A Hubbard; Diana L Miglioretti
Journal:  Biometrics       Date:  2013-02-05       Impact factor: 2.571

9.  Estimating the cumulative risk of a false-positive test in a repeated screening program.

Authors:  Jian-Lun Xu; Richard M Fagerstrom; Philip C Prorok; Barnett S Kramer
Journal:  Biometrics       Date:  2004-09       Impact factor: 2.571

10.  Breast Cancer Screening for Women at Average Risk: 2015 Guideline Update From the American Cancer Society.

Authors:  Kevin C Oeffinger; Elizabeth T H Fontham; Ruth Etzioni; Abbe Herzig; James S Michaelson; Ya-Chen Tina Shih; Louise C Walter; Timothy R Church; Christopher R Flowers; Samuel J LaMonte; Andrew M D Wolf; Carol DeSantis; Joannie Lortet-Tieulent; Kimberly Andrews; Deana Manassaram-Baptiste; Debbie Saslow; Robert A Smith; Otis W Brawley; Richard Wender
Journal:  JAMA       Date:  2015-10-20       Impact factor: 56.272

  10 in total

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