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Nonparametric Methods to Address Scientific Questions in Aging Integrating Real-World and Research Databases

Award Number: R01AG103351
ORGANIZATION: NATIONAL INSTITUTE ON AGING
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 09/03/2026
PERIOD OF PERFORMANCE END DATE: 06/30/2031

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Nonparametric Methods to Address Scientific Questions in Aging Integrating Real-World and Research Databases - PROJECT SUMMARY Electronic health records (EHR) data collected during routine clinical care are a cost-effective data resource to inform studies of disease pathology and prognosis. In aging research focusing on eye-brain connections, there is great interest in using EHR data to develop prognostic tools for eye diseases (e.g., age-related macular degeneration [AMD]), and to understand the relationship between ophthalmic biomarkers and risk of Alzheimer’s disease and related dementias (ADRD). However, in large cohorts such as the Eye Adult Changes in Thought (Eye ACT) study, target variables are often extracted from the EHR, particularly unstructured text in clinical notes, using error-prone text-mining algorithms. As in many other settings, validated data by chart abstraction in Eye ACT are only available on a subset of the cohort. Machine learning methods that leverage detailed data of varying quality can improve accuracy and robustness of analyses to provide reliable scientific and clinical evidence. The proposed research will address three key methodological issues that remain in using machine learning to incorporate error-prone and validated EHR data into analyses to answer scientific questions: 1) improving accuracy and precision of machine-learned inverse-probability weighted (IPW) analyses; and improving design of validation studies for 2) causal inference and 3) prediction model evaluation. Many analyses that combine error-prone and validated data involve IPW analyses of the validated subset. Weights derived using machine learning methods, while useful for prediction, can result in overly variable weights, leading to reduced accuracy or precision of an estimator. This problem can be exacerbated in small samples. We will develop methods that properly incorporate machine learning-based weights, combining advances in generalized raking with targeted learning (e.g., targeted maximum likelihood estimation) for robust and stable weights. When designing data- validation studies, simple random sampling is often inefficient; stratified samples can increase efficiency by validating key subgroups and by utilizing existing optimal design methods that minimize the variance of regression parameter estimates. We will generalize these optimal design approaches to estimate average treatment effects and other targets of causal inference, addressing an important void in the current optimal stratified-design literature. We will further extend these methods to develop the optimal design for evaluating a prediction model and for simultaneously evaluating a model and performing causal inference. By combining advances in machine learning, targeted learning, and survey statistics, we will provide more robust and precise statistical tools to study the eye-brain connection in aging and to generate reliable empirical evidence on actionable factors. Our work using Eye ACT has the potential to transform preventive care for important aging outcomes like AMD and ADRD, while also serving as a statistical framework for improved inference and study design more broadly in biomedical research that leverages observational data with missing or error-prone data.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2026 ( Subtotal = $830,868 )
2026	2026	KAISER FOUNDATION HOSPITALS	1800 HARRISON ST FL 16	OAKLAND	CA	94612	ALAMEDA	USA	Aging Research	000	1	9/2/2026	NEW	$830,868
														Subtotal = $830,868

Grand Total All Awards = $830,868

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Nonparametric Methods to Address Scientific Questions in Aging Integrating Real-World and Research Databases

Award Number: R01AG103351

ORGANIZATION: NATIONAL INSTITUTE ON AGING

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 09/03/2026

PERIOD OF PERFORMANCE END DATE: 06/30/2031

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