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Statistical methods for predicting individualized intervention effects with clustered and longitudinal data

Award Number: R21MH140288
ORGANIZATION: NATIONAL INSTITUTE OF MENTAL HEALTH
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 04/01/2026
PERIOD OF PERFORMANCE END DATE: 03/31/2028

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Statistical methods for predicting individualized intervention effects with clustered and longitudinal data - PROJECT ABSTRACT Identifying individuals likely to respond to a specific intervention is a critical challenge in medical research. In the age of big data, where vast amounts of information are accessible, the potential for personalized interventions based on individual characteristics has become increasingly feasible. However, advancement comes with significant challenges. The sheer volume of data often leads to datasets with numerous factors which might influence outcomes. Moreover, the data may exhibit clustering or repeated measurements, with potentially informative cluster sizes, adding complexity to the analysis. For instance, researchers are interested in understanding why some pregnancies are more vulnerable to maternal immune activation (MIA), which impacts brain and behavioral development in offspring and increases the risk of autism, schizophrenia and other neurodevelopmental disorders. However, this task is complex due to the multitude of biomarkers and clustered or repeatedly measured outcomes over time and brain regions. Current statistical tools available are inadequate for handling the complexities of such data, thus impeding the progress of precision medicine. To address this significant gap, this proposal underscores the urgent need for innovative statistical methodologies that can adeptly handle the complexity of clustered and longitudinal datasets with numerous covariates, thereby advancing the field of precision medicine. By developing novel methods building on our preliminary statistical framework, integrating machine learning techniques, rigorously evaluating these methods through simulation studies that mimic real data, and applying these methodologies to real-world longitudinal and clustered datasets, we aim to make significant contributions to this field. Our preliminary simulation results and real-world examples demonstrate both the scientific merit and computational feasibility of these methods. We will apply these newly developed statistical tools to existing datasets as a proof-of-concept to uncover factors that predict susceptibility and resilience to MIA regarding the brain and behavior development outcomes in offspring. The innovative statistical methods developed hold significant promise for identifying biomarkers that elucidate the link between environmental exposure during human pregnancy and brain mechanisms associated with neurodevelopmental disorders. This advancement will assist in identifying high- risk pregnancies and tailoring interventions for offspring at risk due to MIA exposure. Furthermore, these innovative statistical approaches can be adapted to various interventions and a wide range of medical conditions. We will provide free, user-friendly programs and software to enable research communities to apply these methods easily. Consequently, this project presents a unique opportunity to tackle a complex issue in precision medicine and leverage existing datasets for groundbreaking insights.


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 = $442,750 )
2026	2026	UNIVERSITY OF CALIFORNIA, DAVIS	1850 RESEARCH PARK DR STE 300	DAVIS	CA	95618	YOLO	USA	Mental Health Research Grants	000	1	4/2/2026	NEW	$442,750
														Subtotal = $442,750

Grand Total All Awards = $442,750

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Statistical methods for predicting individualized intervention effects with clustered and longitudinal data

Award Number: R21MH140288

ORGANIZATION: NATIONAL INSTITUTE OF MENTAL HEALTH

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

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

PERIOD OF PERFORMANCE START DATE: 04/01/2026

PERIOD OF PERFORMANCE END DATE: 03/31/2028

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