UCLA

Missing outcome data are common in cluster randomized trials (CRTs) which can complicate inference. Further, the missingness can occur due to dropout of individuals, termed “sporadically” missing data, or dropout of clusters, termed “systematically” missing data, and these two types of missingness could have potentially different missing data mechanisms. We aimed to develop a well-performing and practical approach to handle inference in CRTs when outcome data may be both sporadically and systematically missing. To this end, we first examined the performance of four multilevel multiple imputation (MI) methods to handle sporadically and systematically missing CRT outcome data via a simulation study. Our findings showed that one multilevel MI method which uses the maximum likelihood estimates obtained from a linear mixed model to draw missing values outperformed the others under various scenarios. Using the best performing MI method, we developed methods for conducting sensitivity analysis to test the robustness of inferences under different missing at random (MAR) and missing not at random (MNAR) assumptions. The methods allow for different MNAR assumptions for cluster dropout and individual dropout to reflect that they may arise from different missing data mechanisms. We developed graphical displays to visualize sensitivity analysis results. Our methods are illustrated using a real data application.