TY  - JOUR
A1  - Ultsch, Alfred
A1  - Lötsch, Jörn
T1  - Computed ABC analysis for rational selection of most informative variables in multivariate data
T2  - PLoS One
N2  - Objective: Multivariate data sets often differ in several factors or derived statistical parameters, which have to be selected for a valid interpretation. Basing this selection on traditional statistical limits leads occasionally to the perception of losing information from a data set. This paper proposes a novel method for calculating precise limits for the selection of parameter sets.
Methods: The algorithm is based on an ABC analysis and calculates these limits on the basis of the mathematical properties of the distribution of the analyzed items. The limits implement the aim of any ABC analysis, i.e., comparing the increase in yield to the required additional effort. In particular, the limit for set A, the "important few", is optimized in a way that both, the effort and the yield for the other sets (B and C), are minimized and the additional gain is optimized.
Results: As a typical example from biomedical research, the feasibility of the ABC analysis as an objective replacement for classical subjective limits to select highly relevant variance components of pain thresholds is presented. The proposed method improved the biological interpretation of the results and increased the fraction of valid information that was obtained from the experimental data.
Conclusions: The method is applicable to many further biomedical problems including the creation of diagnostic complex biomarkers or short screening tests from comprehensive test batteries. Thus, the ABC analysis can be proposed as a mathematically valid replacement for traditional limits to maximize the information obtained from multivariate research data.
Y1  - 2015
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/37998
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-379985
SN  - 1932-6203
N1  - Copyright: © 2015 Ultsch, Lötsch. This is an open access article distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
VL  - 10
IS  - (6):e0129767
SP  - 1
EP  - 15
PB  - PLoS
CY  - Lawrence, Kan.
ER  -