Abstract Randomized Controlled Trials (RCTs) are a widely used research protocol in
applied research. Among others, a major challenge of RCTs is the presence of missing data …

Background Missing data are common in randomised controlled trials (RCTs) and can bias
results if not handled appropriately. A statistically valid analysis under the primary missing …

Multiple imputation is a popular method for addressing missing data, but its implementation
is difficult when data have a multilevel structure and one or more variables are …

Many randomized controlled clinical trials compare a continuous outcome variable that is
assessed longitudinally at scheduled follow-up visits between subjects assigned to a …

Background Multiple imputation (MI) is a well-recognised statistical technique for handling
missing data. As usually implemented in standard statistical software, MI assumes that data …

The use of multiple imputation has increased markedly in recent years, and journal
reviewers may expect to see multiple imputation used to handle missing data. However in …

Missing data is an unavoidable issue in controlled clinical trials and public health research
and practice. Presence of missing data and applying inappropriate methods of analysis …

Multiple imputation has become an increasingly popular tech-nique for handling missing
data in studies ranging from cross-sectional to longitudinal studies and from randomized …

Multiple imputation is a promising approach for handling of missing data. One uncertainty in
applications of the multiple imputation to randomized controlled trials with longitudinal data …

Multiple imputation (MI) is a useful tool for conducting analyses under both missing at
random (MAR) and missing not at random (MNAR) assumptions. This chapter discusses …