Abstract Competing risks data are typically encountered in biomedical/epidemiological studies. Examples of such data can be found in studying labor in women as labor can be either spontaneous or due to medical intervention (example: delivery by cesarean) or due to membrane rupture leading to labor. Typically in competing risks framework, each individual is exposed to K distinct types of risks and the eventual failure can be attributed to precisely one of the risks. As is usual in survival data, these competing risks data are further subjected to censoring. Two quantities of considerable interest include, the cause‐specific hazard and the corresponding cumulative incidence function for a specific cause. In this article, we will review various modeling approaches for assessing the effects of covariates through modeling cause‐specific hazard. We will also discuss various approaches for constructing confidence intervals as well as confidence bands for the cause‐specific cumulative incidence function of subjects with given values of the covariates.