  Receiver operating characteristic (ROC) analysis is a tool to evaluate the
capacity of a numeric measure to distinguish between groups, often employed in
the evaluation of diagnostic tests. Overall classification ability is sometimes
crudely summarized by a single numeric measure such as the area under the
empirical ROC curve. However, it may also be of interest to estimate the full
ROC curve while leveraging assumptions regarding the nature of the data
(parametric) or about the ROC curve directly (semiparametric). Although there
has been recent interest in methods to conduct comparisons by way of stochastic
ordering, nuances surrounding ROC geometry and estimation are not widely known
in the broader scientific and statistical community. The overarching goals of
this manuscript are to (1) provide an overview of existing frameworks for ROC
curve estimation with examples, (2) offer intuition for and considerations
regarding methodological trade-offs, and (3) supply sample R code to guide
implementation. We utilize simulations to demonstrate the bias-variance
trade-off across various methods. As an illustrative example, we analyze data
from a recent cohort study in order to compare responses to SARS-CoV-2
vaccination between solid organ transplant recipients and healthy controls.
