In the study of real-world data, the pragmatic randomized controlled trial can provide the optimal evidence for clinical decisions. Although randomization protects against confounding, post-randomization confounding may still arise due to non-compliance. Traditional intention-to-treat analysis will drift apart from true estimation and lead to deviation of clinical decisions. Meanwhile, the alternative traditional methods would subject to bias and confounding. Thus, new methods are required for revolution, i.e., instrument variable method and modern per-protocol analysis. Our study reviews the defects of traditional methods in pragmatic randomized controlled trials, and then refers to two new methods with a detailed discussion of strengths and weaknesses. We aim to provide researches with insights on choosing the statistical methods for pragmatic trial.
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.