Ontology highlight
ABSTRACT: Background
The linear combination of variables is an attractive method in many medical analyses targeting a score to classify patients. In the case of ROC curves the most popular problem is to identify the linear combination which maximizes area under curve (AUC). This problem is complete closed when normality assumptions are met. With no assumption of normality search algorithm are avoided because it is accepted that we have to evaluate AUC n(d) times where n is the number of distinct observation and d is the number of variables.Methods
For d?=?2, using particularities of AUC formula, we described an algorithm which lowered the number of evaluations of AUC from n(2) to n(n-1)?+?1. For d?>?2 our proposed solution is an approximate method by considering equidistant points on the unit sphere in R(d) where we evaluate AUC.Results
The algorithms were applied to data from our lab to predict response of treatment by a set of molecular markers in cervical cancers patients. In order to evaluate the strength of our algorithms a simulation was added.Conclusions
In the case of no normality presented algorithms are feasible. For many variables computation time could be increased but acceptable.
SUBMITTER: Todor N
PROVIDER: S-EPMC4099021 | biostudies-literature | 2014
REPOSITORIES: biostudies-literature
Journal of clinical bioinformatics 20140704
<h4>Background</h4>The linear combination of variables is an attractive method in many medical analyses targeting a score to classify patients. In the case of ROC curves the most popular problem is to identify the linear combination which maximizes area under curve (AUC). This problem is complete closed when normality assumptions are met. With no assumption of normality search algorithm are avoided because it is accepted that we have to evaluate AUC n(d) times where n is the number of distinct o ...[more]