# Flexible  biomarker  evaluation  through  a  cost-effective  data collection mechanism

> **NIH NIH R03** · UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA · 2020 · $73,250

## Abstract

Project Summary/Abstract
There exists an undeniable need for biologists and medical researchers to identify new biomarkers (biological
markers) which are useful in determining exposure and/or for the purposes of disease detection. Consider-
ing the rising cost of assessing new biomarkers, it is reasonable to believe that this area of research has the
potential to be stunted due to funding concerns. My proposal aims at offering these researchers an alternate
data collection mechanism and flexible statistical tools to more cost-effectively identify meaningful biomarkers.
Traditionally, biomarkers are evaluated by collecting specimens from individuals within target populations which
are subsequently tested. Alternatively, the proposed data collection mechanism specifies that several individual
specimens are mixed to form a pool which is then tested. The salient feature of pooling is twofold: (1) one assay
measures several individuals' biomarker levels, which reduces costs; (2) mixing several individual specimens
provides a sufficient amount of the biomarker to avoid the limit of detection.
 The statistical literature surrounding the use of pooling for biomarker assessment predominantly focuses on
the estimation of the receiver operating characteristic (ROC) curve and the area under the curve. Regretfully,
these works assume restrictive assumptions about the underlying distribution of the individual biomarker levels.
If such assumptions are wrong, incorrect inference could be produced. To remedy this limitation, I propose
flexible methods that are robust to those assumptions. Specifically, I will propose a nonparametric estimator
of the ROC curve based on pooled measurements, which will then be used to test whether the biomarker is a
good discriminator (Specific Aim 1). Then I will extend the nonparametric method to to estimate a covariate-
adjusted ROC curve from pooled measurements (Specific Aim 2). The third specific aim will find the optimal
linear combination of multiple biomarkers in order to improve the discriminatory ability. In addition, I will develop
statistical software that easily implements the proposed methods. My proposal is innovative because it offers
methods that could shift the current biomarker research paradigm into a more comprehensive and cost-effective
regime. The successful completion of my proposal could significantly impact biological and medical research
practices, by allowing for the assessment of biomarkers that previously could not be appropriately evaluated due
to cost considerations and the lack of statistically sound methodology.

## Key facts

- **NIH application ID:** 9827533
- **Project number:** 5R03AI135614-02
- **Recipient organization:** UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA
- **Principal Investigator:** Dewei Wang
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $73,250
- **Award type:** 5
- **Project period:** 2018-11-20 → 2021-10-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9827533

## Citation

> US National Institutes of Health, RePORTER application 9827533, Flexible  biomarker  evaluation  through  a  cost-effective  data collection mechanism (5R03AI135614-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/9827533. Licensed CC0.

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