Missing data are a pervasive problem in many public health investigations. The standard approach is to restrict the analysis to subjects with complete data on the variables involved in the analysis. Estimates from such analysis can be biased, especially if the subjects who are included in the analysis are systematically different from those who were excluded in terms of one or more key variables. Severity of bias in the estimates is illustrated through a simulation study in a logistic regression setting. This article reviews three approaches for analyzing incomplete data. The first approach involves weighting subjects who are included in the analysis to compensate for those who were excluded because of missing values. The second approach is based on multiple imputation where missing values are replaced by two or more plausible values. The final approach is based on constructing the likelihood based on the incomplete observed data. The same logistic regression example is used to illustrate the basic concepts and methodology. Some software packages for analyzing incomplete data are described.