Model-based Approaches for Bias Correction Due to Missing Data and Measurement Error in Self-reported Measures of Dietary Intake and Device-based Measures of Physical Activity in Obesity Studies

Oregon Convention Center 

Self-reported measures of episodically consumed foods are often used in dietary assessments; however, they are prone to excess zeroes and measurement errors associated with periods of non-consumption and recall bias, respectively. Similarly, wearable devices enable the continuous monitoring of physical activity (PA) but generate complex functional data prone to excess zeroes associated with periods of inactivity and with poorly characterized systematic errors. In this work, we propose semicontinuous modeling approaches for correcting biases due to measurement errors and missing data associated with functional and scalar covariates prone to excess zeroes and classical measurement error in sparse conditional functional quantile regression models. Our proposed semicontinuous models are composed of two parts, the first part is associated with periods of inactivity or non-wear and excess zeroes, while the second component is associated with the observed non-zero measures. We assume zero inflated exponential family models for the error prone device-based PA and self-reported dietary data and develop semicontinuous zero-inflated methods for bias corrections.