This study is carried out using a random sample obtained from data collected during the period from 2010 Mourão M.
The receiver operating characteristic (ROC) curve is the most widely used measure for evaluating the discriminatory performance of a continuous biomarker.
A popular topic in medical research today is the development of markers to classify subjects as diseased or disease free, as high or low risk, or in terms of treatment response or another future event.
These markers may be the results of, for example, genetic or proteomic evaluations, imaging techniques, bacterial culture, or risk factor information.
Individuals who are disease-free earlier may develop the disease later due to longer study follow-up, and also their marker value may change from baseline during follow-up.
The classical (standard) approach of ROC curve analysis considers event (disease) status and marker value for an individual as fixed over time, however in practice, both the disease status and marker value change over time.
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In this paper, the authors demonstrate the need for covariate adjustment in studies of classification accuracy, discuss methods for adjusting for covariates, and distinguish covariate adjustment from several other related, but fundamentally different, uses for covariates.
They draw analogies and contrasts throughout with studies of association.