Multiple imputation has been previously applied for mass imputation, that is, the imputation from a subsample with complete information to a larger sample. In such applications, the missing data rates are often substantial, such as 80 percent or more, but valid inferences should, in principle, be within reach when probability sampling is used. Yet, limitations of multiple imputation can become ...

Multiple imputation is a technique for handling data sets with missing values. The method 0lls in each missing value several times, creating many augmented data sets. Each augmented data set is analyzed separately and the results combined to give a 0nal result consisting of an estimate and a measure of uncertainty. In this paper we consider nonparametric multiple-imputation methods to handle mi...

Often a binary variable is generated by dichotomizing an underlying continuous variable measured at a specific time point according to a prespecified threshold value. In the event that the underlying continuous measurements are from a longitudinal study, one can use the repeated-measures model to impute missing data on responder status as a result of subject dropout and apply the logistic regre...

Less than optimum strategies for missing values can produce biased estimates, distorted statistical power, and invalid conclusions. After reviewing traditional approaches (listwise, pairwise, and mean substitution), selected alternatives are covered including single imputation,multiple imputation, and full information maximum likelihood estimation. The effects of missing values are illustratedf...

Missing data is one of the genuine challenges we face during analysis of clinical trials. The main impact of missing data is that it can infuse bias in results which reduces the chance of getting the appropriate interpretation. Hence proper knowledge of techniques for handling missing data is crucial. A common method of handling this problem is by imputing missing values. There can be single or...

Statistical agencies that disseminate data to the public are ethically and often legally required to protect the confidentiality of respondents’ identities and sensitive attributes. To satisfy these requirements, Rubin (1993), Little (1993), and Fienberg (1994) proposed that agencies utilize multiple imputation. For example, agencies can release the units originally surveyed with some values, s...

BACKGROUND Longitudinal studies almost always have some individuals with missing outcomes. Inappropriate handling of the missing data in the analysis can result in misleading conclusions. Here we review a wide range of methods to handle missing outcomes in single and repeated measures data and discuss which methods are most appropriate. METHODS Using data from a randomized controlled trial to...

It is well known that if a multivariate outlier has one or more missing component values, then multiple imputation methods tend to impute non-extreme values and make the outlier become less extreme and less likely to be detected. In this paper, nonparametric depthbased multivariate outlier identifiers are used as criteria in a numerical study comparing several established methods of multiple im...