Project Summary / Abstract One of the greatest challenges in cancer and nutritional epidemiology is the uncertainty in covariate measurements, which is a major source of bias in research aimed at elucidating causal relationships between diet and cancer incidence and mortality. Dietary intake is usually estimated using a self-report food-frequency questionnaire, and this dietary assessment is often subject to substantial measurement error. The issue of exposure uncertainty lead to the potential for considerable bias in estimated health effects, masking our ability to detect true associations. Right-censored survival outcomes are common in epidemiologic practice, and the Cox regression model is typically used to model such outcomes. Regression calibration is a simple measurement error adjustment method in Cox regression, and is often used in cancer and nutritional epidemiology. However, when the degree of measurement error is large and the regression coefficient of the error-prone covariate is large, regression calibration is unsatisfactory for bias correction due to exposure uncertainty. We will develop an improved regression calibration method considering the degree of measurement error and the coefficient of the error-prone covariate simultaneously in the calibration procedure. The Expectation-Maximization (EM) algorithm has been remarkably applied for a wide variety of situations for incomplete data problems. However, EM does not receive much attention to deal with measurement error problems in survival data. We will develop a complete treatment of EM in main / validation studies to fill an important gap in the literature. User-friendly publicly available software development will be a central feature accompanying all new methods to be developed.