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A new outlier identification test for method comparison studies based on robust regression

Standard

A new outlier identification test for method comparison studies based on robust regression. / Rauch, Geraldine; Geistanger, Andrea; Timm, Jürgen.

in: J BIOPHARM STAT, Jahrgang 21, Nr. 1, 01.2011, S. 151-169.

Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung

Harvard

Rauch, G, Geistanger, A & Timm, J 2011, 'A new outlier identification test for method comparison studies based on robust regression', J BIOPHARM STAT, Jg. 21, Nr. 1, S. 151-169. https://doi.org/10.1080/10543401003650275

APA

Rauch, G., Geistanger, A., & Timm, J. (2011). A new outlier identification test for method comparison studies based on robust regression. J BIOPHARM STAT, 21(1), 151-169. https://doi.org/10.1080/10543401003650275

Vancouver

Rauch G, Geistanger A, Timm J. A new outlier identification test for method comparison studies based on robust regression. J BIOPHARM STAT. 2011 Jan;21(1):151-169. https://doi.org/10.1080/10543401003650275

Bibtex

@article{8fe9d8247f5e47169906ebdf54f39493,
title = "A new outlier identification test for method comparison studies based on robust regression",
abstract = "The identification of outliers in method comparison studies (MCS) is an important part of data analysis, as outliers can indicate serious errors in the measurement process. Common outlier tests proposed in the literature usually require a homogeneous sample distribution and homoscedastic random error variances. However, datasets in MCS usually do not meet these assumptions. In this work, a new outlier test based on robust linear regression is proposed to overcome these special problems. The LORELIA (local reliability) residual test is based on a local, robust residual variance estimator, given as a weighted sum of the observed residuals. The new test is compared to a standard test proposed in the literature by a Monte Carlo simulation. Its performance is illustrated in examples.",
keywords = "Analysis of Variance, Clinical Trials as Topic, Confidence Intervals, Evaluation Studies as Topic, Humans, Linear Models, Monte Carlo Method, Research Design, Journal Article",
author = "Geraldine Rauch and Andrea Geistanger and J{\"u}rgen Timm",
year = "2011",
month = jan,
doi = "10.1080/10543401003650275",
language = "English",
volume = "21",
pages = "151--169",
journal = "J BIOPHARM STAT",
issn = "1054-3406",
publisher = "Taylor & Francis",
number = "1",

}

RIS

TY - JOUR

T1 - A new outlier identification test for method comparison studies based on robust regression

AU - Rauch, Geraldine

AU - Geistanger, Andrea

AU - Timm, Jürgen

PY - 2011/1

Y1 - 2011/1

N2 - The identification of outliers in method comparison studies (MCS) is an important part of data analysis, as outliers can indicate serious errors in the measurement process. Common outlier tests proposed in the literature usually require a homogeneous sample distribution and homoscedastic random error variances. However, datasets in MCS usually do not meet these assumptions. In this work, a new outlier test based on robust linear regression is proposed to overcome these special problems. The LORELIA (local reliability) residual test is based on a local, robust residual variance estimator, given as a weighted sum of the observed residuals. The new test is compared to a standard test proposed in the literature by a Monte Carlo simulation. Its performance is illustrated in examples.

AB - The identification of outliers in method comparison studies (MCS) is an important part of data analysis, as outliers can indicate serious errors in the measurement process. Common outlier tests proposed in the literature usually require a homogeneous sample distribution and homoscedastic random error variances. However, datasets in MCS usually do not meet these assumptions. In this work, a new outlier test based on robust linear regression is proposed to overcome these special problems. The LORELIA (local reliability) residual test is based on a local, robust residual variance estimator, given as a weighted sum of the observed residuals. The new test is compared to a standard test proposed in the literature by a Monte Carlo simulation. Its performance is illustrated in examples.

KW - Analysis of Variance

KW - Clinical Trials as Topic

KW - Confidence Intervals

KW - Evaluation Studies as Topic

KW - Humans

KW - Linear Models

KW - Monte Carlo Method

KW - Research Design

KW - Journal Article

U2 - 10.1080/10543401003650275

DO - 10.1080/10543401003650275

M3 - SCORING: Journal article

C2 - 21191861

VL - 21

SP - 151

EP - 169

JO - J BIOPHARM STAT

JF - J BIOPHARM STAT

SN - 1054-3406

IS - 1

ER -