# Statistics Core

> **NIH NIH U19** · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · 2024 · $753,850

## Abstract

PROJECT SUMMARY
ED-LEAD is proposing an embedded pragmatic clinical trial of three independent yet potentially synergistic
interventions all targeted at improving the care of Persons Living with Dementia (PLWD) and their care
partners. The three interventions – emergency care redesign, nurse-led telephonic care, and community
paramedicine – all focus on PLWD who present to the Emergency Department (ED) for care and the need for
careful attention to care transitions in the triadic encounter between the PLWD, care partner, and healthcare
team. These interventions will share patient-level outcomes that will benefit from a joint analysis. The proposed
randomization structure will be based on a multifactorial design, where EDs will be randomized to any
combination of the three interventions. This design will generate substantial quantities of data that will need to
be evaluated to assess implementation fidelity of each intervention and assess a range of intervention-specific
and universal outcomes. The Statistical Analysis Core (SAC) will provide biostatistical expertise for the overall
project. The SAC’s key function is to develop the modeling framework that will enable the study team to
evaluate each intervention individually and in combination with others. The factorial design is a key element of
the joint study that will allow the investigators to explore how the interventions might work together to have a
greater impact than any single intervention. Traditional methods of statistical inference typically require very
large sample sizes to perform complex factorial experiments. Furthermore, unreasonable assumptions
regarding the absence of interaction effects are sometimes required in analyzing a factorial design. We have
developed a Bayesian modeling approach that will enable us to present the results in a way that will allow
health care providers, health care systems, and health policy makers to assess the individual and joint impacts
of these three very different interventions never evaluated simultaneously. With the analytic framework serving
as a foundation, the SAC will support data management related to patient-level health utilization data, training
and intervention fidelity, non-CMS, intervention-specific patient-level outcome measurement, and intervention-
specific implementation outcome measurement. The SAC will oversee the randomization process to ensure
that the 80 ED sites participating in the study are distributed across the eight arms of the factorial design in a
way that minimizes imbalance of key site-level characteristics, such as location and size. The SAC will perform
statistical analyses and data exploration using appropriate statistical and computing methodologies, and assist
in interpreting and presenting results.

## Key facts

- **NIH application ID:** 10929969
- **Project number:** 5U19AG078105-02
- **Recipient organization:** NEW YORK UNIVERSITY SCHOOL OF MEDICINE
- **Principal Investigator:** Keith Goldfeld
- **Activity code:** U19 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $753,850
- **Award type:** 5
- **Project period:** 2023-09-15 → 2028-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10929969

## Citation

> US National Institutes of Health, RePORTER application 10929969, Statistics Core (5U19AG078105-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10929969. Licensed CC0.

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