Pragmatic randomized controlled trials can provide high-quality evidence. However, pragmatic trials need to frequently encounter the missing outcome data due to the challenges of quality assurance and control. The missing outcome could lead to bias which may misguide the conclusions. Thus, it is crucial to handle the missing outcome data appropriately. Our study initially summarized the bias structures and missingness mechanisms, and then reviewed important methods based on the assumption of missing at random. We referred to the multiple imputations and inverse probability of censoring weighting for dealing with missing outcomes. This paper aimed to provide insights on how to choose the statistical methods on missing outcome data.