Literature DB >> 9871951

Correcting for verification bias in studies of a diagnostic test's accuracy.

X H Zhou1.   

Abstract

Standard methods for assessing the accuracy of diagnostic tests require determination of true disease status for each study patient. In practice, some study patients might not have verified disease status. If the decision to verify a patient is influenced by the test result, analysis using only verified cases might lead to biased results, commonly known as verification bias. This paper reviews recent developments in bias-correction methods for studies on the accuracy of diagnostic tests.

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Year:  1998        PMID: 9871951     DOI: 10.1177/096228029800700403

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


  29 in total

1.  Bias.

Authors:  Miguel Delgado-Rodríguez; Javier Llorca
Journal:  J Epidemiol Community Health       Date:  2004-08       Impact factor: 3.710

2.  Evaluation of a records-review surveillance system used to determine the prevalence of autism spectrum disorders.

Authors:  Rachel Nonkin Avchen; Lisa D Wiggins; Owen Devine; Kim Van Naarden Braun; Catherine Rice; Nancy C Hobson; Diana Schendel; Marshalyn Yeargin-Allsopp
Journal:  J Autism Dev Disord       Date:  2011-02

3.  Evaluating imaging and computer-aided detection and diagnosis devices at the FDA.

Authors:  Brandon D Gallas; Heang-Ping Chan; Carl J D'Orsi; Lori E Dodd; Maryellen L Giger; David Gur; Elizabeth A Krupinski; Charles E Metz; Kyle J Myers; Nancy A Obuchowski; Berkman Sahiner; Alicia Y Toledano; Margarita L Zuley
Journal:  Acad Radiol       Date:  2012-02-03       Impact factor: 3.173

4.  An examination of the dynamic changes in prostate-specific antigen occurring in a population-based cohort of men over time.

Authors:  Brant A Inman; Jingyu Zhang; Nilay D Shah; Brian T Denton
Journal:  BJU Int       Date:  2012-02-07       Impact factor: 5.588

5.  Bias in estimating accuracy of a binary screening test with differential disease verification.

Authors:  Todd A Alonzo; John T Brinton; Brandy M Ringham; Deborah H Glueck
Journal:  Stat Med       Date:  2011-04-15       Impact factor: 2.373

6.  Global identifiability of latent class models with applications to diagnostic test accuracy studies: A Gröbner basis approach.

Authors:  Rui Duan; Ming Cao; Yang Ning; Mingfu Zhu; Bin Zhang; Aidan McDermott; Haitao Chu; Xiaohua Zhou; Jason H Moore; Joseph G Ibrahim; Daniel O Scharfstein; Yong Chen
Journal:  Biometrics       Date:  2019-11-06       Impact factor: 2.571

7.  Validation of a Nurse-Based Delirium-Screening Tool for Hospitalized Patients.

Authors:  Anita Hargrave; Jesse Bastiaens; James A Bourgeois; John Neuhaus; S Andrew Josephson; Julia Chinn; Melissa Lee; Jacqueline Leung; Vanja Douglas
Journal:  Psychosomatics       Date:  2017-07-24       Impact factor: 2.386

8.  A new method to address verification bias in studies of clinical screening tests: cervical cancer screening assays as an example.

Authors:  Xiaonan Xue; Mimi Y Kim; Philip E Castle; Howard D Strickler
Journal:  J Clin Epidemiol       Date:  2013-12-12       Impact factor: 6.437

9.  A hybrid Bayesian hierarchical model combining cohort and case-control studies for meta-analysis of diagnostic tests: Accounting for partial verification bias.

Authors:  Xiaoye Ma; Yong Chen; Stephen R Cole; Haitao Chu
Journal:  Stat Methods Med Res       Date:  2014-05-26       Impact factor: 3.021

10.  ROC Curve Analysis in the Presence of Imperfect Reference Standards.

Authors:  Peizhou Liao; Hao Wu; Tianwei Yu
Journal:  Stat Biosci       Date:  2016-07-19
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