… Missing data is a common occurrence in clinical research. Missing data occurs when the
value … Common approaches to addressing the presence of missing data include complete-case …

… Clinical trials comparing different treatments on participants often involve repeated … problems
of missing data. One common source of missingness arises when participants no longer …

… can successfully handle missing values and offer some possible future research direction. …
In this research estimation with missing values become a problem of computing marginal …

… that dealt with missing data using the antediluvian and … missing data treatments in the first
edition, I’d hoped we’d see most researchers embrace the modern treatments for missing data. …

… of missing data, who have heard of multiple imputation as a method to deal with missing data,
but … in their deliberations regarding their statistical analyses when faced with missing data. …

… A key finding of this study is that the proportion of missing data should not be used as a …
bias and improve efficiency for analysis of MAR data at any proportion of missingness. If we …

… We provide guidance on choice of analysis when data are incomplete. Using … by missing
data and compare MI and CCA, with respect to bias and efficiency, in a range of missing data …

… clinical trials, imputation of missing data introduces risks of bias due to the nature of the missing
data and the way it is handled… review; however, data is considered missing completely at …

… enlighten the clinical trials community, especially as they relate to emerging issues in clinical
trials, such as new types of clinical trial designs. The oversight of clinical trials remains an …

… , engineering applications, clinical trials, etc. For … lost to follow-up, occurrences of missing
values in the data are inevitable. While fitting the model to large datasets, often, missing values …