Access the full text.
Sign up today, get DeepDyve free for 14 days.
Loading next page...
/lp/wiley/evaluating-bias-in-method-comparison-studies-using-linear-regression-140m5Ya1vX
References (25)
-
P. Sprent (1971)
Models in regression and related topics -
D. Lindley (1947)
Regression Lines and the Linear Functional Relationship, 9
-
R. Carroll, D. Ruppert (1996)
The Use and Misuse of Orthogonal Regression in Linear Errors-in-Variables ModelsThe American Statistician, 50
-
P. Rosenthal, C. Edwards, David Penney (1982)
Calculus and Analytic Geometry -
J. Riu, F. Rius (1996)
Assessing the accuracy of analytical methods using linear regression with errors in both axes.Analytical chemistry, 68 11
-
J. Wolfowitz, A. Mood (1951)
An Introduction to the Theory of StatisticsNature, 87
-
C. Hartmann, J. Smeyers‐Verbeke, W. Penninckx, Y. Heyden, P. Vankeerberghen, D. Massart (1995)
Reappraisal of hypothesis testing for method validation : detection of systematic error by comparing the means of two methods or of two laboratoriesAnalytical Chemistry, 67
-
L. Chan, T. Mak (1979)
Maximum Likelihood Estimation of a Linear Structural Relationship with ReplicationJournal of the royal statistical society series b-methodological, 41
-
Ángel Martínez, J. Riu, F. Rius (2000)
Lack of fit in linear regression considering errors in both axesChemometrics and Intelligent Laboratory Systems, 54
-
E. Saouter, B. Blattmann (1994)
Analyses of organic and inorganic mercury by atomic fluorescence spectrometry using a semiautomatic analytical systemAnalytical Chemistry, 66
-
(1986)
Applique Âe a Á l'Exploitation des Mesures -
G. Hahn, W. Meeker, Luis Escobar (1991)
Statistical Intervals: A Guide for Practitioners -
E. Ziegel (1987)
Practical Statistics for Analytical Chemists -
J. Mandel, F. Linning (1957)
Study of Accuracy in Chemical Analysis Using Linear Calibration CurvesAnalytical Chemistry, 29
-
D. Schafer, Kathleen Purdy (1996)
Likelihood analysis for errors-in-variables regression with replicate measurementsBiometrika, 83
-
J. Mandel (1984)
Fitting Straight Lines When Both Variables are Subject to ErrorJournal of Quality Technology, 16
-
P. Meier, Lisa Alaniz (2005)
Statistical Methods in Analytical ChemistryTechnometrics, 47
-
Oscar Güell, J. Holcombe (1990)
Analytical Applications of Monte Carlo Techniques.Analytical Chemistry, 62
-
Chi-Lun Cheng, J. Ness (1994)
On Estimating Linear Relationships When Both Variables are Subject to ErrorsJournal of the royal statistical society series b-methodological, 56
-
C. Hartmann, J. Smeyers‐Verbeke, W. Penninckx, D. Massart (1997)
Detection of bias in method comparison by regression analysisAnalytica Chimica Acta, 338
-
M. Creasy (1956)
CONFIDENCE LIMITS FOR THE GRADIENT IN THE LINEAR FUNCTIONAL RELATIONSHIPJournal of the royal statistical society series b-methodological, 18
-
Ángel Martínez, F. Río, J. Riu, F. Rius (1999)
Detecting proportional and constant bias in method comparison studies by using linear regression with errors in both axesChemometrics and Intelligent Laboratory Systems, 49
-
P. Voogt, J. Hinschberger, E. Maier, B. Griepink, H. Muntau, J. Jacob (1996)
Improvements in the determination of eight polycyclic aromatic hydrocarbons through a stepwise interlaboratory study approachFresenius' Journal of Analytical Chemistry, 356
-
J. Lisý, A. Cholvadová, J. Kutej (1990)
Multiple straight-line least-squares analysis with uncertainties in all variablesComput. Chem., 14
-
J. Riu, F. Rius (1995)
Univariate regression models with errors in both axesJournal of Chemometrics, 9
- Publisher
- Wiley
- Copyright
- Copyright © 2002 John Wiley & Sons, Ltd.
- ISSN
- 0886-9383
- eISSN
- 1099-128X
- DOI
- 10.1002/cem.669
- Publisher site
- See Article on Publisher Site
Abstract
This paper presents a theoretical background for estimating the probability of committing a β error when checking the presence of method bias. Results obtained at different concentration levels from the analytical method being tested are compared by linear regression with the results from a reference method. Method bias can be detected by applying the joint confidence interval test to the regression line coefficients from a bivariate least squares (BLS) regression technique. This finds the regression line considering the errors in the two methods. We have validated the estimated probabilities of β error by comparing them with the experimental values from 24 simulated data sets. We also compared the probabilities of β error estimated using the BLS regression method on two real data sets with those estimated using ordinary least squares (OLS) and weighted least squares (WLS) regression techniques for a given level of significance α. We found that there were important differences in the values predicted with WLS and OLS compared to those predicted with the BLS regression method. Copyright © 2002 John Wiley & Sons, Ltd.
Journal
Journal of Chemometrics – Wiley
Published: Jan 1, 2002