In many fields of applications, data on some variables under study may be difficult or expensive to collect, causing an important bottleneck in research work. To bypass the difficulty, the two-stage sampling design has proved to be a useful strategy. With this sampling design, at stage Ⅰ of the study data on some variables that are easy to measure are collected for all study subjects, while full and exact data are obtained only for a selected subset of the whole sample at stage Ⅱ. Consequently, data collected under a two-stage study are ”incomplete” in the sense that the data are incompletely observed for all subjects, except for members selected at stage Ⅱ. The set-up can thus be viewed as a special type of ”missing” or ”measurement error” data. Regression analysis under two stage sampling design has been an active research area in statistical literature for two decades. This paper briefly reviews the existing methodologies for this problem, and indicates potential future research directions in this area.