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.
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.
Thus, an ROC curve as a function of time is more appropriate.
However, many researchers still use the standard ROC curve approach to determine the marker capability ignoring the time dependency of the disease status or the marker.
We comprehensively review currently proposed methodologies of time-dependent ROC curves which use single or longitudinal marker measurements, aiming to provide clarity in each methodology, identify software tools to carry out such analysis in practice and illustrate several applications of the methodology.
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These markers may be the results of, for example, genetic or proteomic evaluations, imaging techniques, bacterial culture, or risk factor information.
Oftentimes, factors other than disease affect marker observations.
For example, levels of prostate-specific antigen (PSA), a biomarker widely used to screen men for prostate cancer, tend to increase with age.
ROC (receiver operating characteristic) curve analysis is well established for assessing how well a marker is capable of discriminating between individuals who experience disease onset and individuals who do not.
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.