Literature DB >> 18945590

Various randomized designs can be used to evaluate medical tests.

Jeroen G Lijmer1, Patrick M M Bossuyt.   

Abstract

OBJECTIVE: To explore designs for evaluating the prognostic and predictive value of medical tests and their effect on patient outcome. STUDY
DESIGN: Theoretical analysis with examples from the medical literature.
RESULTS: For evaluating the prognostic value of a test, one can include the test at baseline in prognostic studies. To evaluate the value of test in predicting treatment outcome, the test results can be used as baseline information in randomized controlled trials of treatment. To compare the prognostic or predictive value of two or more tests, the test result combinations can be used as baseline information. To evaluate the effect on patient outcome, randomized controlled trials of test strategies are an option. Randomization can apply to all tested or be restricted to specific subgroups, such as those with discordant test results, to increase the efficiency of trials.
CONCLUSION: The prognostic and predictive value of medical tests can and should be evaluated, to demonstrate the test's ability to guide clinical decision making and to improve patient outcome. Various randomized designs can be used to evaluate the effects on testing on patient outcome.

Entities:  

Mesh:

Year:  2008        PMID: 18945590     DOI: 10.1016/j.jclinepi.2008.06.017

Source DB:  PubMed          Journal:  J Clin Epidemiol        ISSN: 0895-4356            Impact factor:   6.437


  20 in total

1.  Design-related bias in estimates of accuracy when comparing imaging tests: examples from breast imaging research.

Authors:  Nehmat Houssami; Stefano Ciatto
Journal:  Eur Radiol       Date:  2010-09       Impact factor: 5.315

2.  Diagnostic test accuracy study of 18F-sodium fluoride PET/CT, 99mTc-labelled diphosphonate SPECT/CT, and planar bone scintigraphy for diagnosis of bone metastases in newly diagnosed, high-risk prostate cancer.

Authors:  Randi F Fonager; Helle D Zacho; Niels C Langkilde; Joan Fledelius; June A Ejlersen; Christian Haarmark; Helle W Hendel; Mine Benedicte Lange; Mads R Jochumsen; Jesper C Mortensen; Lars J Petersen
Journal:  Am J Nucl Med Mol Imaging       Date:  2017-11-01

3.  Evaluation of Diagnostic Tests.

Authors:  Brendan J Barrett; John M Fardy
Journal:  Methods Mol Biol       Date:  2021

4.  Added value of a serum proteomic signature in the diagnostic evaluation of lung nodules.

Authors:  Chad V Pecot; Ming Li; Xueqiong J Zhang; Rama Rajanbabu; Ciara Calitri; Aaron Bungum; James R Jett; Joe B Putnam; Carol Callaway-Lane; Steve Deppen; Eric L Grogan; David P Carbone; John A Worrell; Karel G M Moons; Yu Shyr; Pierre P Massion
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2012-02-28       Impact factor: 4.254

5.  Modelling of OGTT curve identifies 1 h plasma glucose level as a strong predictor of incident type 2 diabetes: results from two prospective cohorts.

Authors:  Akram Alyass; Peter Almgren; Mikael Akerlund; Jonathan Dushoff; Bo Isomaa; Peter Nilsson; Tiinamaija Tuomi; Valeriya Lyssenko; Leif Groop; David Meyre
Journal:  Diabetologia       Date:  2014-10-08       Impact factor: 10.122

Review 6.  Methodologic approaches to evaluating new highly sensitive diagnostic tests: avoiding overdiagnosis.

Authors:  Joris A H de Groot; Christiana A Naaktgeboren; Johannes B Reitsma; Karel G M Moons
Journal:  CMAJ       Date:  2016-07-04       Impact factor: 8.262

7.  Assessing treatment-selection markers using a potential outcomes framework.

Authors:  Ying Huang; Peter B Gilbert; Holly Janes
Journal:  Biometrics       Date:  2012-02-02       Impact factor: 2.571

Review 8.  The role of PET and PET-CT scanning in assessing response to neoadjuvant therapy in esophageal carcinoma.

Authors:  Milly Schröer-Günther; Fülöp Scheibler; Robert Wolff; Marie Westwood; Brigitta Baumert; Stefan Lange
Journal:  Dtsch Arztebl Int       Date:  2015-08-17       Impact factor: 5.594

9.  Designing a study to evaluate the benefit of a biomarker for selecting patient treatment.

Authors:  Holly Janes; Marshall D Brown; Margaret S Pepe
Journal:  Stat Med       Date:  2015-06-25       Impact factor: 2.373

10.  Efficiency of study designs in diagnostic randomized clinical trials.

Authors:  Bo Lu; Constantine Gatsonis
Journal:  Stat Med       Date:  2012-10-15       Impact factor: 2.373

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