… , in particular, establishing the prevalence of missing data and describing the current handling
… guidelines for handling missing values in the analysis of cluster randomised trials, and for …

… also used in the FDA analyses to address missing data. The goal of this article is to illustrate
challenges and considerations associated with the handling of missing data in clinical trials. …

… The goal of the current study was to examine various missing data assumptions and … This
study examined the effect of 5 methods for handling missing data under a variety of conditions …

… Finally, we would like to reiterate that the best way to handle missing data is to … missing
data in a study, the less the extent of missing data, the more robust and convincing the study …

… We consider handling missing data arising due to (i) participant infection, (ii) treatment
disruptions and (iii) loss to follow-up. We consider both settings where treatment effects for a ‘…

… (Citation2016), we apply the missing data handling methods investigated in the current …
data from a subgroup of this study. In the selected subgroup of this Phase III clinical trial, there …

… , researchers will find that studies in a research synthesis … missing data that occur in a
research synthesis, and discuss strategies synthesists can use when faced with missing data…

… We discuss the ML as a method for handling missing data. Examples for applying ML to
missing data problems can be found in Little and Rubin (2002). Furthermore, in a variety of …

… of missing data in clinical research and their relationships to specific methods of handling
missing data, and 2) to describe several simple, yet naive, methods for handling missing data …

… These results suggest that, at least when missingness is unrelated to treatment assignment,
all of the approaches we evaluated for handling missing data are adequate for protecting the …