This project discusses some of the methodologies developed over the years to estimate attributable risk among exposed persons and the attributable risk in the entire population (also called Etiologic Fraction). It provides a general framework for estimating attributable risk among the exposed (denoted lambda_e). By making use of the recent observation that the two measures of attributable risk can be linked through the prevalence of the risk factor among the cases (denoted V_x), an estimate of population attributable risk (denoted lambda) for matched case-control studies is determined. Using the methodology developed recently by Kuritz and Landis (1987), this project provides explicit formulas for estimating the attributable risk among the exposed and the population attributable risk, and their large sample variances. This has been done both in situations where exactly R controls have been matched to a case and for a variable number of controls per case. The methodologies are illustrated with data from some case-control studies reported in the literature. Asymptotic relative efficiencies of different matching designs computed in terms of the costs of gathering cases and controls, are presented, together with some recommendations on what design is considered optimal. / Thesis / Master of Science (MSc)