Standard analysis of matched-pair cohort data requires information only from pairs in which at least one had the study outcome. This can be useful in traffic fatality studies of characteristics that can vary among vehicle occupants, such as seat belt use, as crash databases often lack information about vehicles in which all survived. However, matching crash victims who were in the same vehicle does not necessarily eliminate confounding by vehicle or crash related factors, because the matched occupants must be in different seat positions. This paper reviews three methods for estimating relative risks in matched-pair crash data. The first, Mantel-Haenszel stratified methods, may produce biased estimates if seat position is associated with the outcome. The second, the double-pair comparison method, was designed to deal with confounding by seat position. If the effects of seat position vary according to some vehicle or crash characteristic which is associated with the study exposure, adjustment for this characteristic may be needed to produce unbiased estimates. Third, conditional Poisson regression and Cox proportional hazards regression can produce unbiased estimates, but may require model interaction terms between seat position and vehicle or crash characteristics. This paper reviews some of the strengths and limitations of each of these methods, and illustrates their use in simulated and real crash data.