# Statistical methods to characterize patients who highly benefit across multifaceted clinical outcomes, from treatments in Alzheimers Disease and Related Dementias (ADRD)

> **NIH NIH R01** · JOHNS HOPKINS UNIVERSITY · 2024 · $471,932

## Abstract

Project Summary
Methods to characterize patients who highly benefit on multiple clinical outcomes, from treatments in Alzheimer's
disease and related dementias (ADRD), are necessary to treat patients effectively. Treatments may benefit some
patients on targeted outcomes, but harm some patients on other, e.g., cognitive, outcomes. So, characterizing
patients who highly benefit on multiple outcomes is significant: first it allows these patients to choose a treatment
if it is predicted to give them high benefits without the harms; second, accurate characterization methods do not
exist. Generally, a characterization method has two stages. One stage “constructs” outcome predictions based
on patients' covariates; and another stage “synthesizes” the predictions to estimate the goal - a large high benefit
group. As the “construction” stage uses many covariates, it needs methods to estimate predictions from a model,
i.e., from a large set of possible distributions (e.g., regression, neural networks). These predictions are then used
in the “synthesis” stage for the goal. Such existing methods, however, do not use the clinical goal (to characterize
high-benefit patients) as a guide inside the construction stage. For a single outcome, recent work has shown that
this lack of linking can produce dramatically inaccurate characterizations, no matter the model.
 For characterizing patients with multiple high benefits, new methods must explicitly link all multiple clinical
goals (i.e. high benefits in all outcomes) in the construction stage. In preparatory work, we showed that existing
methods for multiple outcomes, can miss even most of the high benefit patients, and we developed a preliminary
better method by establishing the missing links. This new project is motivated by our ongoing work with two
studies. The first study tested if citalopram reduces agitation in Alzheimer's patients. Since citalopram may harm
cognitive function, we set to characterize patients with high citalopram effect in (a) reducing agitation and (b)
maintaining cognitive function. The second study tests the effect of transcranial direct current stimulation on
primary progressive aphasia outcomes, with related goals. In preparatory work, we found strong evidence that
standard methods miss up to 70% of the patients with multiple high benefits, compared to the new methods. For
this project we propose to fully develop methods to characterize patients who highly benefit on multiple outcomes.
The methods will be applied to the above studies, and can help more generally in other ADRD studies.
Aim 1. Develop methods to characterize patients who highly-benefit in multiple outcomes in randomized
trials. These methods are significant because they allow accurate personalized treatment choices.
Aim 2. Develop methods to find if a simpler subset of the full multiple outcomes, can have similar patient
characterization as the full outcomes. These methods are significant because they can suggest if a high-effect
on earlier...

## Key facts

- **NIH application ID:** 10881026
- **Project number:** 1R01AG083423-01A1
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** CONSTANTINE E FRANGAKIS
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $471,932
- **Award type:** 1
- **Project period:** 2024-05-01 → 2028-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10881026

## Citation

> US National Institutes of Health, RePORTER application 10881026, Statistical methods to characterize patients who highly benefit across multifaceted clinical outcomes, from treatments in Alzheimers Disease and Related Dementias (ADRD) (1R01AG083423-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10881026. Licensed CC0.

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