# Analysis of Alzheimer's disease studies that feature truncated or interval-censored covariates

> **NIH NIH R21** · UNIVERSITY OF MASSACHUSETTS AMHERST · 2024 · $238,405

## Abstract

SUMMARY
More than 6.5 million Americans suffer from Alzheimer’s Disease (AD), and by 2050 this number is expected to
double. Yet the development of effective therapies remains an urgent unmet need. In a scenario of highly
complex AD pathophysiology and costly and long drug development process, repurposing of drugs approved for
other indications is an attractive complementary approach. The rationale for repurposing a drug initially relies
on observational studies demonstrating that the cumulative drug exposure is correlated with either a reduction
in the risk of developing AD dementia, or with a slowing of the rate of cognitive decline based on serial cognitive
evaluations, or with milder AD neuropathological changes at autopsy examination, after adjusting for covariates
including age, sex, education, family history of dementia and APOE genotype, and vascular and other modifiable
risk factors. However, the vast majority of such published longitudinal studies have ignored the truncation or
interval censoring associated with the covariate of interest (e.g., cumulative drug exposure), which is due to
either termination of observation by death or non-continuous observation visits in longitudinal studies. Building
upon our extensive prior work in the areas of truncation and censoring as well as AD, here we propose to develop
methods to more appropriately treat these sampling and measurement problems to avoid bias. We will apply
them in two case studies of drugs with opposite purported associations with AD risk -- statins (protective) and
proton-pump inhibitors (PPIs, deleterious) -- but mixed findings from longitudinal studies. To this end, we will
leverage the strengths of two high-quality publicly available longitudinal datasets: the National Alzheimer’s
Coordinating Center (NACC) cohort study and the Harvard Aging Brain Study (HABS). In Aim 1, we propose
analytic methods that remove biases arising due to covariate measurements that are truncated in AD studies,
such as cumulative statin exposure, including inverse probability weighting, pseudo-observations and reverse
regression approaches. In Aim 2, we develop pseudo-observation methods for time-to-event regression with
interval-censored covariates. In Aim 3, we conduct and report analyses of NACC and HABS datasets using
proposed methods, and develop publicly available R packages for implementation of our proposed methods.
Successful completion of these specific aims will produce new statistical methodology that will eliminate the bias
that may arise with truncated and interval-censored covariates, which are inherent to longitudinal cohort studies
in AD and related dementias. Our proposed research is responsive to the NIA Notice of Special Interest (NOSI):
Maximizing the Scientific Value of Secondary Analyses of Existing Cohorts and Datasets in Order to Address
Research Gaps and Foster Additional Opportunities in Aging Research (NOT-AG-21-020).

## Key facts

- **NIH application ID:** 10901990
- **Project number:** 5R21AG083468-02
- **Recipient organization:** UNIVERSITY OF MASSACHUSETTS AMHERST
- **Principal Investigator:** Jing Qian
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $238,405
- **Award type:** 5
- **Project period:** 2023-08-15 → 2026-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10901990

## Citation

> US National Institutes of Health, RePORTER application 10901990, Analysis of Alzheimer's disease studies that feature truncated or interval-censored covariates (5R21AG083468-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10901990. Licensed CC0.

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