  The area under the receiver operating characteristic curve (AUC) is often
used to evaluate the performance of clinical prediction models. Recently, a
more refined strategy has been proposed to examine a partial area under the
curve (pAUC), which can account for differing costs associated with false
negative versus false positive results. Such consideration can substantially
increase the clinical utility of prediction models depending on the clinical
question. Properties of the pAUC estimator create significant challenges for
pAUC-optimal marker selection and model building. As such, current approaches
towards these aims can be complex and computationally intensive. We present a
simpler method based on weighted logistic regressions. We refer to our strategy
as logistic push, due to shared heuristics with the ranking algorithm P-norm
push. Logistic push is particularly useful in the high-dimensional setting,
where fast and broadly available algorithms for fitting penalized regressions
can be used for both marker selection and model fitting.
