This chapter addresses issues in study designs and methods applied in the specific field of occupational epidemiology such as dose-response analysis, healthy worker effect (or bias), and exposure assessment. Strengths and limitations of different study designs when applied to the occupational context are also addressed, including cross-sectional, cohort, and case-control studies, either industry-based or general population-based, including mortality odds ratios studies. Methodological issues covered in the chapter include application of quantitative industry-specific job-exposure matrices with Bayesian decision analysis to workers’ individual job histories; and qualitative and quantitative assessment of confounding in occupational studies. The section on the healthy worker effect has been updated to cover the most recent theoretical advances, especially in reference to the healthy worker survivor bias: directed acyclic graphs (DAGs) have been added to illustrate situations in which employment status behaves as a time-varying confounder affected by previous exposure. In these cases, traditional statistical methods fail to control for the healthy worker survival bias. A new section is devoted to the estimation of disease advancement, a topic often neglected in textbooks which represents an alternative to relative risk and may have some advantages in risk communication. Three general methods to calculate disease advancement are introduced, including classical accelerated failure time (AFT) models, risk and rate advancement periods (RAP), and G (generalized) models. Finally, the last section describes how occupational epidemiology can help to evaluate the need and effectiveness of primary prevention interventions and policies by using the example of occupational cancer.