In order to investigate causality in situations where random assignment is not possible, propensity scores can be used in regression adjustment, stratification, inverse-probability treatment weighting, or matching. The basic concepts behind propensity scores have been extensively described. When data are longitudinal or missing, the estimation and use of propensity scores become a challenge. Traditional methods of propensity score estimation delete cases listwise. Missing data estimation, by multiple imputation, can be used to alleviate problems due to missing values, if performed correctly. Longitudinal studies are another situation where propensity score use may be difficult because of attrition and needing to account for data when propensities may vary over time. This article discusses the issues of missing data and longitudinal designs in the context of propensity scores. The syntax, datasets, and output used for these examples are available on http://jea.sagepub.com/content/early/recent for readers to download and follow.