Measurement error and information bias are ubiquitous in epidemiology, yet directed acyclic graphs (DAGs) are infrequently used to represent them, in contrast with confounding and selection bias. This represents a missed opportunity to leverage the full utility of DAGs to depict associations between the variables we actually analyse in practice: empirically measured variables, which are necessarily measured with error. In this article, we focus on applying causal diagrams to depict the data-generating mechanisms that give rise to the data we analyse, including measurement error.
View Article and Find Full Text PDFImportance: The risk of developing Parkinson disease (PD) after objective hearing loss is unknown. PD studies using self-reported hearing loss are insensitive, and objective data are lacking.
Objective: To examine the association of hearing loss with incident PD in US veterans and its effect modification by well-established prodromal conditions and hearing aids.
Otolaryngol Head Neck Surg
November 2024
Objective: Examine associations between military blast exposures on hearing loss and self-reported hearing difficulties among Active-Duty Service Members (ADSM) and Veterans from the Noise Outcomes in Servicemembers Epidemiology (NOISE) study.
Study Design: Cross-sectional.
Setting: Multi-institutional tertiary referral centers.
Objective: To describe associations between a history of traumatic brain injury (TBI) and the severity of tinnitus-related functional impairment among a national, stratified random sample of veterans diagnosed with tinnitus by the Department of Veterans Affairs (VA) healthcare system.
Setting: A multimodal (mailed and internet) survey administered in 2018. Participants: VA healthcare-using veterans diagnosed with tinnitus; veterans with comorbid TBI diagnosis were oversampled.