The performance of a marker is evaluated by the area under the ROC curve (AUC) in which a higher AUC value indicates a better marker performance. The AUC is also equal to the probability of a diseased individual having a higher marker value than a healthy individual [8].

The goal of this article is to propose a new direct method that provides straightforward inference for AUC(t) and IAUC. Our method directly models AUC(t) as a function of time t using a flexible and yet parsimonious parametric model, namely, the fractional polynomials model proposed by Royston and Altman (1994).

For the purpose of estimating the time‐dependent AUC and IAUC, the “indirect” methods have some drawbacks. For example, most ROC‐estimation procedures rely on strong parametric assumptions such as proportional hazards, which may lead to potential bias due to the misspecification of the time‐to‐event model. In addition, because these methods are “indirect,” they are not particularly efficient and require unnecessary intensive computation.

everal time‐dependent measures have been proposed in the literature, including time‐dependent sensitivity, specificity, ROC and AUC (Heagerty, Lumley, and Pepe, 2005; Heagerty and Zheng, 2005).

student union group kind of took it over

the vote of no confidence

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Professor Sean McMahon?