Biomarker-assisted reporting in nutritional epidemiology: addressing measurement error in exposure–disease associations

In nutritional epidemiology, self-reported dietary data are commonly used to investigate diet-disease relationships. However, the resulting association estimates are often subject to biases due to random and systematic measurement errors. Regression calibration has emerged as a crucial method for addressing these biases by refining self-reported nutrient intake with objective biomarkers, which differ from the true values only by a random "noise" component. This paper presents methodological tools for analyzing nutritional epidemiology cohort studies involving time-to-event data when a biomarker subsample is available alongside dietary assessments. We introduce novel regression calibration methods to tackle two common challenges in this field. First, a widely used approach assumes that the log hazard ratio (HR) follows a linear function of dietary exposure. However, assessing whether this assumption holds-or if a more flexible model is needed to capture potential deviations from linearity-is often necessary. Second, another prevalent analytical strategy involves estimating HRs based on categorized dietary exposure variables. New methods are critically needed to minimize bias in defining category boundaries and estimating hazard ratios within exposure categories, both of which can be distorted by measurement error. We apply these methods to reassess the relationship between sodium and potassium intake and cardiovascular disease risk using data from the Women's Health Initiative.