Truncated robust distance for clinical laboratory safety data monitoring and assessment.
Laboratory safety data are routinely collected in clinical studies for safety monitoring and assessment. We have developed a truncated robust multivariate outlier detection method for identifying subjects with clinically relevant abnormal laboratory measurements. The proposed method can be applied to historical clinical data to establish a multivariate decision boundary that can then be used for future clinical trial laboratory safety data monitoring and assessment. Simulations demonstrate that the proposed method has the ability to detect relevant outliers while automatically excluding irrelevant outliers. Two examples from actual clinical studies are used to illustrate the use of this method for identifying clinically relevant outliers.
Quantitative Sciences, GlaxoSmithKline Pharmaceuticals R&D, Collegeville, Pennsylvania 19426, USA. xiwu.2.lin@gsk.com
Xiwu
X
Lin
Daniel
D
Parks
Lei
L
Zhu
Lloyd
L
Curtis
Helen
H
Steel
Andrew
A
Rut
Vincent
V
Mooser
Lon
L
Cardon
Alan
A
Menius
Kwan
K
Lee
D015415:Biomarkers / Q000097:blood; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D016903:Drug Monitoring / Q000706:statistics & numerical data*; D064420:Drug-Related Side Effects and Adverse Reactions; D006801:Humans; D008077:Lipoproteins, LDL / Q000097:blood; D008111:Liver Function Tests; D008954:Models, Biological; D015233:Models, Statistical; D015999:Multivariate Analysis; D012449:Safety / Q000706:statistics & numerical data; D014280:Triglycerides / Q000097:blood
D015415:Biomarkers; D008077:Lipoproteins, LDL; D014280:Triglycerides