# Statistical Methods for Evaluating Markers for Treatment Selection

> **NIH NIH R01** · FRED HUTCHINSON CANCER RESEARCH CENTER · 2020 · $429,338

## Abstract

Project Summary
Biomarkers that predict the effects of therapeutic or preventative treatments hold great promise for im-
proving health outcomes and decreasing medical costs. For example, if a treatment is thought likely to
benefit only a subset of subjects, a biomarker that identifies these subjects can be used to recommend
the treatment to them, and allow others to pursue alternatives. Potential treatment selection biomarkers,
also called predictive or prescriptive biomarkers, are being produced in abundance due to technological
advancements and a heightened interest in “personalized medicine”, and yet a comprehensive statistical
framework for study design and analysis of biomarker performance is lacking. We propose to build on a
successful research program to develop novel statistical methods for marker identification and evaluation,
with the ultimate goal of advancing such a framework. Aim 1 develops novel statistical analysis methods
for discovering and evaluating markers for guiding treatment. The ideal setting for marker evaluation is
a randomized and controlled trial. For this setting we will develop methods that address challenges not
accommodated by existing methodology. For early-phase marker studies, which are typically not random-
ized trials, methods for evaluating a biomarker's potential performance do not yet exist and our research
will fill this gap. Methods will also be developed that incorporate medical cost data into the evaluation of
a biomarker. Aim 2 will develop novel study designs for discovering and evaluating markers for guiding
treatment. A basic first step in study design is identifying what is the desired performance of the biomarker,
and we will develop techniques for this. Study designs will then be developed to assess whether a marker
achieves this standard. Early-phase studies– either cohort studies or single arm trials– will be sized to
evaluate a marker's potential performance. Novel randomized trial designs will be developed to definitively
assess marker performance and will require smaller sample sizes than existing designs. A sequence of
studies will be put forth for developing a biomarker, from early-phase studies of potential performance to
late-phase studies of actual performance. The research will be conducted by an inter-disciplinary team
of investigators with extensive expertise in biomarker evaluation, clinical trial design and analysis, health
economics, and clinical research. Collaborations with cooperative groups such as the Early Detection Re-
search Network, focused on biomarker discovery and evaluation, and SWOG, focused on clinical trials of
cancer prevention and therapeutic strategies, will ensure that the research has immediate application and
impact. Studies to which the methods will be applied include one that aims to identify women at high risk for
epithelial ovarian cancer who can be recommended prophylactic removal of the fallopian tubes at the time
of hysterectomy; and another that seeks to i...

## Key facts

- **NIH application ID:** 9825510
- **Project number:** 5R01CA152089-10
- **Recipient organization:** FRED HUTCHINSON CANCER RESEARCH CENTER
- **Principal Investigator:** Holly Janes
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $429,338
- **Award type:** 5
- **Project period:** 2010-07-01 → 2021-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9825510

## Citation

> US National Institutes of Health, RePORTER application 9825510, Statistical Methods for Evaluating Markers for Treatment Selection (5R01CA152089-10). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9825510. Licensed CC0.

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