  Biomarker is a critically important tool in modern clinical diagnosis,
prognosis, and classification/prediction. However, there are fiscal and
analytical barriers to biomarker research. Selective Genotyping is an approach
to increasing study power and efficiency where individuals with the most
extreme phenotype (response) are chosen for genotyping (exposure) in order to
maximize the information in the sample. In this article, we describe an
analogous procedure in the biomarker testing landscape where both response and
biomarker (exposure) are continuous. We propose an intuitive reverse-regression
least squares estimator for the parameters relating biomarker value to
response. Monte Carlo simulations show that this method is unbiased and
efficient relative to estimates from random sampling when the joint normal
distribution assumption is met. We illustrate application of proposed methods
on data from a chronic pain clinical trial.
