# DISCOVERY - Statistics and Analysis Core

> **NIH NIH U19** · MASSACHUSETTS GENERAL HOSPITAL · 2024 · $3,703,471

## Abstract

ABSTRACT (STATISTICS CORE)
The Statistical Core, led by Drs. Gottesman and Wruck, will provide key analytic plans and completion of key
analyses, power and sample size calculations, and design of the cognitive evaluation and cognitive outcome
assessment. This core will support the DISCOVERY Network application and associated cores in the design of
the study and also for the detailed cognitive measurement that is a key component of the overall study design,
as well as in the ultimate evaluation and communication of the final study results. This Core will facilitate the
study aims, and for its cognitive assessments and classification, it will use methods previously used in the
Atherosclerosis Risk in Communities (ARIC) study, where the Core PIs Drs. Gottesman and Wruck have
previously collaborated, and which are relevant to disparity populations in the assessment of long-term cognitive
trajectories and outcomes. In addition to standard methods to consider, time-to-dementia and longitudinal
cognitive trajectory analyses, this Core will propose novel methods for predictive modeling, including the use of
machine learning and deep learning techniques. The DISCOVERY Statistics Core will: 1) develop and implement
a feasible neurocognitive battery at the DISCOVERY Network clinical sites, with repeated measurements over
at least a 2-year follow-up period; 2) conduct adjudication of cognitive events, including MCI and dementia, using
the neurocognitive battery described in Aim 1, and to perform validation of dementia classification using the
shorter battery in those participants undergoing both the shorter and more comprehensive battery; 3) contribute
statistical expertise to the design and operation of the DISCOVERY project. This will include power calculations
and sampling plans to ensure optimal representation of disparity populations and stroke subtypes for the overall
cohort and for sub-studies, semi-annual quality control checks of study data, a quality assurance plan for the
neurocognitive battery, and statistical input on OSMB reports; 4) develop an analytic strategy and perform
statistical analyses for the specific aims and sub-aims of the DISCOVERY project. Statistics core investigators
will provide methodological expertise in analysis of time-to-event endpoints (dementia, MCI); cognitive change
analyses; domain-specific performance and change analyses; and novel machine learning approaches to
predictive modeling of PSCID, incorporating neuroimaging, biomarker, “omics” and cognitive longitudinal
measurements. Through this Core, state-of-the-art cognitive measurement methods and statistical analysis
methodologies will allow unbiased consideration of cognitive outcomes in this proposed cohort of stroke patients.

## Key facts

- **NIH application ID:** 10929419
- **Project number:** 5U19NS115388-06
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** Rebecca F Gottesman
- **Activity code:** U19 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $3,703,471
- **Award type:** 5
- **Project period:** 2019-09-19 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10929419

## Citation

> US National Institutes of Health, RePORTER application 10929419, DISCOVERY - Statistics and Analysis Core (5U19NS115388-06). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10929419. Licensed CC0.

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