# Statistical methods for enriched clinical trials with applications to Alzheimer's disease research

> **NIH NIH F31** · UNIVERSITY OF CALIFORNIA-IRVINE · 2023 · $38,577

## Abstract

PROJECT SUMMARY / ABSTRACT
With the rising prevalence of Alzheimer’s disease (AD) in the U.S. and worldwide, there is a crucial need for
preventative and disease-modifying treatments. Randomized controlled clinical trials (RCTs) serve as the gold
standard to determine whether a candidate treatment has a favorable benefit-to-risk ratio for a pre-specified
target patient population. However, heterogeneity of treatment effects across subpopulations (e.g., due to
health disparities) may yield medical interventions that are not one-size-fits-all. Enrichment strategies are
commonly employed in RCTs to identify the target populations most likely to benefit from a candidate treatment
and/or have the outcome of interest during the course of the trial. Enrichment in AD RCTs aligns with the
National Plan to Address AD Strategy 1.B to expand research to develop disease-modifying treatments.
Currently, there is a gap in the understanding of RCTs using enrichment and adaptations to the randomized
treatment assignment allocations (response-adaptive enrichment), especially for RCTs with a repeated
measures (longitudinal, e.g., changes in activities of daily living scores) or censored (time-to-event, e.g., time
to dementia) primary outcome. Application of standard statistical methods to enrichment designs may,
however, result in bias (tendency to systematically over- or under- estimate treatment effects). Biased
estimates can lead to approval of less effective therapies, in the best case, and approval of potentially harmful
or ineffective therapies or missing an effective therapy, in the worst case, as a consequence of over- or under-
estimating treatment effects. Our conjecture is that the bias induced in a fixed enrichment pre-post (only two
assessments; one pre- and one post-randomization) RCT will be exacerbated when using response-adaptive
enrichment in longitudinal or time-to-event RCTs. The applicant’s long-term objective as a collaborator on
RCTs and independent researcher is to provide well-calibrated and valid statistical inference for complex
innovative designs to facilitate drug development in AD and other diseases. This F31 proposal aims to quantify
the impact of enrichment (e.g., on bias), and as needed, develop novel statistical methods to obtain valid
inference in enriched RCTs with a longitudinal primary outcome (Aim 1) and a time-to-event primary outcome
(Aim 2). Simulation studies using data from completed, large phase 3 NIA- and industry-sponsored mild
cognitive impairment and AD trials will be used to empirically validate the newly developed theory and methods
in real-world settings. To provide resources for trialists, freely-available and user-friendly software based on
Aims 1-2 will be developed (Aim 3) as an extension to the existing RCTdesign (www.rctdesign.org) R package,
co-authored by the sponsor of this application. Research findings from Aims 1-2 will be disseminated via
conference presentations and peer-reviewed publications. Succes...

## Key facts

- **NIH application ID:** 10607649
- **Project number:** 1F31AG077880-01A1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA-IRVINE
- **Principal Investigator:** NAVNEET RAM HAKHU
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $38,577
- **Award type:** 1
- **Project period:** 2023-09-25 → 2025-05-24

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10607649

## Citation

> US National Institutes of Health, RePORTER application 10607649, Statistical methods for enriched clinical trials with applications to Alzheimer's disease research (1F31AG077880-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10607649. Licensed CC0.

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