Multivariate regression models allow one to compare the statistical strength of associations among several risk factors in the presence of markers and co-factors. Power is usually increased when using regression models compared to simple univariate comparisons. For continuous endpoints, inclusion of important independent variables in the regression equation serves to reduce the error variance for all other comparisons. For logistic regression, there is also a bias in estimation of the odds ratio, but the direction of the bias can be positive or negative. Thus, regression models are important because they increase the efficiency of proposed comparisons. However, it is required to insure that there are sufficient numbers of participants to allow regression analyses to take place. LTRC investigators will use the convention of having at least 10 observations for each planned regressor (independent variable) in a multivariate analysis to insure that the sample size is adequate for this type of analysis.