# Novel Bayesian assessments of device-based physical activity and self-reported dietary intake in joint models of all-cause mortality and type 2 diabetes in a cohort of biracial older US adults

> **NIH NIH R01** · TRUSTEES OF INDIANA UNIVERSITY · 2024 · $608,855

## Abstract

Biomedical research often involves the collection of error-prone, complex, high-dimensional functional and scalar
data. Functional data analysts typically treat functional data as smooth latent curves obtained at discrete time
intervals and regard the error terms as homoscedastic and independent random noise, ignoring potential serial
correlations. Although measurement error attenuates coefficients in classical regression, the impacts of
heteroscedastic, error-prone functional covariates and the combination of error-prone functional and scalar
covariates in survival models, censored quantile regression, and joint models of censored and uncensored data
remain unknown. Failing to account for measurement error and serial correlations in functional covariates in
linear regression leads to severely biased estimates, influencing conclusions drawn from such models. Although
researchers have worked extensively to correct for error-prone scalar covariates in survival models, they have
not addressed measurement error in complex, high-dimensional functional data and the mixture of errors in
combined scalar and functional data in survival and censored quantile regression models. We will develop novel
approaches to survival analysis using Bayesian and frequentist frameworks to address the complexities
introduced by measurement errors in longitudinal, high-dimensional, device-based physical activity (PA) and
self-reported dietary intake (DI) data. Current wearable devices monitor PA objectively, but generate complex
data with poorly understood, heteroscedastic, and systematic errors. Also, researchers often assess DI with self-
reports, which are prone to recall bias and variations in seasonal and day-to-day intake. We will build and
evaluate new models with data from the Reasons for Geographic and Racial Differences in Stroke (REGARDS)
Study, a biracial cohort of adults aged 45 years or older (n=30,239). All models will correct for error in device-
measured PA and self-reported DI. We will pursue three aims. Aim 1: Develop latent group semiparametric
models for survival time correcting for measurement error in DI and PA. Aim 2: Develop model-based
approaches to correct for measurement error in PA and DI data and determine the relationships of error-
corrected PA and DI data with joint quantile functions of censored survival time. Aim 3: Develop models to
estimate the average treatment effect (ATE) of T2D on survival time after correcting for measurement error in
PA and DI in the presence of endogeneity and potential asymmetric dependency. Our models will inform
personalized recommendations for PA and DI and address health disparities in vulnerable populations. We will
make public use software that implements our models and allows other researchers to apply our methods in
diverse biomedical settings.

## Key facts

- **NIH application ID:** 10882916
- **Project number:** 1R01DK136994-01A1
- **Recipient organization:** TRUSTEES OF INDIANA UNIVERSITY
- **Principal Investigator:** Roger Sai Zoh
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $608,855
- **Award type:** 1
- **Project period:** 2024-05-01 → 2028-02-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10882916

## Citation

> US National Institutes of Health, RePORTER application 10882916, Novel Bayesian assessments of device-based physical activity and self-reported dietary intake in joint models of all-cause mortality and type 2 diabetes in a cohort of biracial older US adults (1R01DK136994-01A1). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10882916. Licensed CC0.

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