Comparing Imperfect Measurements with the Bland-Altman Technique: Application in Gene Expression Analysis. Ohno-Machado, L., Vinterbo, S., Dreiseitl, S., Jenssen, T., & Kuo, W. JAMIA, Suppl. S:572–6, 2002. abstract bibtex Several problems in medicine and biology involve the comparison of two measurements made on the same set of cases. The problem differs from a calibration problem because no gold standard can be identified. Testing the null hypothesis of no relationship using measures of association is not optimal since the measurements are made on the same cases, and therefore correlation coefficients will tend to be significant. The descriptive Bland-Altman method can be used in exploratory analysis of this problem, allowing the visualization of gross systematic differences between the two sets of measurements. We utilize the method on three sets of matched observations and demonstrate its usefulness in detecting systematic variations between two measurement technologies to assess gene expression.
@article{Ohno-Machado2002,
title = {Comparing Imperfect Measurements with the {{Bland-Altman}} Technique: Application in Gene Expression Analysis.},
author = {{Ohno-Machado}, Lucila and Vinterbo, Staal and Dreiseitl, Stephen and Jenssen, Tor-Kristian and Kuo, Winston},
year = {2002},
journal = {JAMIA},
volume = {Suppl. S},
pages = {572--6},
abstract = {Several problems in medicine and biology involve the comparison of two measurements made on the same set of cases. The problem differs from a calibration problem because no gold standard can be identified. Testing the null hypothesis of no relationship using measures of association is not optimal since the measurements are made on the same cases, and therefore correlation coefficients will tend to be significant. The descriptive Bland-Altman method can be used in exploratory analysis of this problem, allowing the visualization of gross systematic differences between the two sets of measurements. We utilize the method on three sets of matched observations and demonstrate its usefulness in detecting systematic variations between two measurement technologies to assess gene expression.},
copyright = {All rights reserved},
pii = {1833},
pubmedid = {12463888},
keywords = {12463888,Algorithms,Anonymous Testing,Artificial Intelligence,Bias (Epidemiology),Carcinoma,Child,Comparative Study,Computational Biology,Computerized,Confidentiality,Data Interpretation,Databases,Diagnosis,Differential,Disclosure,DNA,Gene Expression,Gene Expression Profiling,Gene Expression Regulation,Genetic Markers,Humans,Internet,Logistic Models,Lung Neoplasms,Medical Records Systems,Messenger,Multivariate Analysis,Neoplasm,Neoplasms,Neoplastic,Neural Networks (Computer),Non-U.S. Gov't,Oligonucleotide Array Sequence Analysis,P.H.S.,Privacy,Research Support,Rhabdomyosarcoma,RNA,Sarcoma,Small Cell,Software,Statistical,U.S. Gov't}
}
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S","pages":"572–6","abstract":"Several problems in medicine and biology involve the comparison of two measurements made on the same set of cases. The problem differs from a calibration problem because no gold standard can be identified. Testing the null hypothesis of no relationship using measures of association is not optimal since the measurements are made on the same cases, and therefore correlation coefficients will tend to be significant. The descriptive Bland-Altman method can be used in exploratory analysis of this problem, allowing the visualization of gross systematic differences between the two sets of measurements. 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