… HANDLING MISSING DATA IN STATISTICAL … of missing data, statistical analyses often
involve missing data imputation. In order to maintain the objectivity of the missing data handling …

… to different missing data assumptions. Medical journals can help to improve the situation by
requiring higher standards of reporting and analytical methods to deal with missing data, and …

… that serious missing data problems result from a trial design … ciency by using intentionally
incomplete data. It also discusses … , rather than resolve missing data issues. Intentionality is a …

… Furthermore, it is likely that, in clinical research, the two types of missing data are mixed
and it is very difficult to assess which type is (most) present. When using the method of LOCF, …

… validity of research results may be dependent on investigators’ approach to handling missing
data, … Using multiple imputation for analysis of incomplete data in clinical research. Nursing …

… used to handle missing data depends on the mechanism responsible for the data being
missing. … cohorts (as this would lead to additional complexities in handling missing data). Where …

… missing data in a clinical trial as much as possible (Figure 1). Efforts to decrease missing data
must begin with the design of the trial… or repeated measures data should be clearly defined …

… Research Council study provides recommendations for limiting missing data in clinical trial
design … robustness of findings to alternative assumptions about the missing data. A Food and …

… missing data. Complete case analysis was the most common method for handling missing
data … individual observations due to missing data, at the initial phase of study inclusion or at …

… of missing values due to dropout or unreadable images. There are no uniform ways to handle
missing radiographic data, … We also evaluate four other missing data imputation methods …