# Statistical developments for biomarker and diagnostic test evaluation with applications to Alzheimer's disease

> **NIH NIH F30** · UNIVERSITY OF KANSAS MEDICAL CENTER · 2022 · $34,428

## Abstract

Project Summary
 Agreement studies, used in the evaluation of newly-developed biomarkers and diagnostic tests, depend
upon statistical methods proposed nearly 50 years ago. As Alzheimer’s disease has entered into an era where
neuroimaging biomarkers are incorporated into the diagnostic strategy, the progression of our statistical
methods must match that of our medical growth. These statistical methods contain significant flaws, such as
depending on sample disease prevalence, having complex interpretations, imposing restrictive experimental
designs and failing to account for risk factors. The current proposal seeks to advance the statistical methods
used in the development of biomarkers and diagnostics, and, in doing so, will identify characteristics important
for diagnosing and predicting Alzheimer’s disease status.
 We hypothesize that our novel statistical contributions will correct these flaws in our approach to biomarker
and diagnostic test development. It will allow for a more informative, interpretable and robust way to quantify
the agreement and accuracy of medical tests. For our first aim, we will develop a novel statistical methodology
to quantify agreement, centering our framework on mixed effect models. We will compare our approach with
traditional methods among providers evaluating neuroimaging biomarkers. We will leverage data from the NIA-
funded R01 Alzheimer’s Prevention through Exercise (APEx) study and enhance it with primary data collection
of neuroimaging interpretations by a diverse sample of providers. For our second aim, we will determine how
the sampling design of contemporary agreement studies influence predictive accuracy. Simulation “in-silico”
studies will be performed, reproducing many scenarios present in the literature. Additionally, data from the
Alzheimer’s Disease Neuroimaging Initiative (ADNI) will be leveraged and measures informing accuracy, such
as sensitivity and specificity, will be calculated. We will do so under traditional approaches and our novel
statistical framework, demonstrating that current methods are merely a special case of our more robust model.
This will highlight limitations of current approaches.
 The broad, long term objectives of this proposal are two-fold. First, we aim to develop a robust and
generalized statistical method for evaluating agreement and accuracy of biomarkers and diagnostic tests. By
using mixed effects models and corresponding sampling designs, we will overcome flaws present in traditional
approaches and gain advantages, such as easily interpreted measures and generalizable results. The second
objective is to facilitate a rigorous training plan that will provide a foundation for my future career as a
physician-statistician who focuses on biomarker methodology. While the combination of these objectives aligns
with the purpose of my current MD-PhD program and this NIH Pre-doctoral Training F30 award, the statistical
advancements are applicable to any medical condition, and ...

## Key facts

- **NIH application ID:** 10399993
- **Project number:** 5F30AG071349-02
- **Recipient organization:** UNIVERSITY OF KANSAS MEDICAL CENTER
- **Principal Investigator:** Katelyn A McKenzie
- **Activity code:** F30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $34,428
- **Award type:** 5
- **Project period:** 2020-12-30 → 2024-12-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10399993

## Citation

> US National Institutes of Health, RePORTER application 10399993, Statistical developments for biomarker and diagnostic test evaluation with applications to Alzheimer's disease (5F30AG071349-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10399993. Licensed CC0.

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