Literature DB >> 3589244

Comparison of quantitative diagnostic tests: type I error, power, and sample size.

K Linnet.   

Abstract

For a quantitative laboratory test the 0.975 fractile of the distribution of reference values is commonly used as a discrimination limit, and the sensitivity of the test is the proportion of diseased subjects with values exceeding this limit. A comparison of the estimates of sensitivity between two tests without taking into account the sampling variation of the discrimination limits can increase the type I error to about seven times the nominal value of 0.05. Correct statistical procedures are considered, and the power and required sample size are studied for Gaussian and log-Gaussian distributions of diagnostic test values. The results may be useful for the planning phase of studies to evaluate quantitative diagnostic tests.

Mesh:

Year:  1987        PMID: 3589244     DOI: 10.1002/sim.4780060207

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  13 in total

1.  Parametric and non-parametric confidence intervals of the probability of identifying early disease stage given sensitivity to full disease and specificity with three ordinal diagnostic groups.

Authors:  Tuochuan Dong; Lili Tian; Alan Hutson; Chengjie Xiong
Journal:  Stat Med       Date:  2011-12-05       Impact factor: 2.373

2.  Semiparametric Inference for ROC Curves with Censoring.

Authors:  Hua Liang; Yong Zhou
Journal:  Scand Stat Theory Appl       Date:  2008-06-01       Impact factor: 1.396

Review 3.  Receiver Operating Characteristic (ROC) Curve Analysis for Medical Diagnostic Test Evaluation.

Authors:  Karimollah Hajian-Tilaki
Journal:  Caspian J Intern Med       Date:  2013

4.  Development of an activity disease score in patients with uveitis (UVEDAI).

Authors:  Esperanza Pato; Mª Auxiliadora Martin-Martinez; Adela Castelló; Rosalía Méndez-Fernandez; Santiago Muñoz-Fernández; Miguel Cordero-Coma; Lucia Martinez-Costa; Elia Valls; Miguel Reyes; Félix Francisco; Mar Esteban; Alex Fonollosa; Fernando Sanchez-Alonso; Cruz Fernández-Espartero; Teresa Diaz-Valle; José Miguel Carrasco; Emma Beltran-Catalán; Marisa Hernández-Garfella; María Victoria Hernández; Laura Pelegrin; Ricardo Blanco; David Diaz-Valle
Journal:  Rheumatol Int       Date:  2016-11-04       Impact factor: 2.631

5.  The Impact of Data Dependence on Speaker Recognition Evaluation.

Authors:  Jin Chu Wu; Alvin F Martin; Craig S Greenberg; Raghu N Kacker
Journal:  IEEE/ACM Trans Audio Speech Lang Process       Date:  2016-09-30

6.  Validation of Nonparametric Two-Sample Bootstrap in ROC Analysis on Large Datasets.

Authors:  Jin Chu Wu; Alvin F Martin; Raghu N Kacker
Journal:  Commun Stat Simul Comput       Date:  2015-08-31       Impact factor: 1.118

7.  Comparison of two correlated ROC curves at a given specificity or sensitivity level.

Authors:  Leonidas E Bantis; Ziding Feng
Journal:  Stat Med       Date:  2016-06-20       Impact factor: 2.373

8.  Empirical Likelihood-Based Confidence Interval of ROC Curves.

Authors:  Haiyan Su; Yongsong Qin; Hua Liang
Journal:  Stat Biopharm Res       Date:  2009-11-01       Impact factor: 1.452

9.  Confidence Interval Estimation for Sensitivity to the Early Diseased Stage Based on Empirical Likelihood.

Authors:  Tuochuan Dong; Lili Tian
Journal:  J Biopharm Stat       Date:  2014-11-05       Impact factor: 1.051

10.  Confidence interval estimation of the difference between two sensitivities to the early disease stage.

Authors:  Tuochuan Dong; Le Kang; Alan Hutson; Chengjie Xiong; Lili Tian
Journal:  Biom J       Date:  2013-11-22       Impact factor: 2.207

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