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 …

Missing data due to loss to follow‐up or intercurrent events are unintended, but unfortunately
inevitable in clinical trials. Since the true values of missing data are never known, it is …

Multiple imputation has become one of the most popular approaches for handling missing
data in statistical analyses. Part of this success is due to Rubin's simple combination rules …

Objectives Previous research has shown that while missing data are common in
bioarchaeological studies, they are seldom handled using statistically rigorous methods …

In clinical trials of treatments for chronic pain, the percentage of participants who withdraw
early can be as high as 50%. Major reasons for early withdrawal in these studies include …

Clinical trials with longitudinal outcomes typically include missing data due to missed
assessments or structural missingness of outcomes after intercurrent events handled with a …

The analysis of time‐to‐event data typically makes the censoring at random assumption, ie,
that—conditional on covariates in the model—the distribution of event times is the same …

Control‐based pattern mixture models (PMM) and delta‐adjusted PMMs are commonly used
as sensitivity analyses in clinical trials with non‐ignorable dropout. These PMMs assume …

Reference-based multiple imputation methods have become popular for handling missing
data in randomized clinical trials. Rubin's variance estimator is well known to be biased …

Missing data are a common issue in cost‐effectiveness analysis (CEA) alongside
randomised trials and are often addressed assuming the data are 'missing at random' …