Multiple imputation is a straightforward method for handling missing data in a principled
fashion. This paper presents an overview of multiple imputation, including important …

Multiple imputation by chained equations (MICE) is an effective tool to handle missing data-
an almost unavoidable problem in quantitative data analysis. However, despite the empirical …

Missing data commonly occur in large epidemiologic studies. Ignoring incompleteness or
handling the data inappropriately may bias study results, reduce power and efficiency, and …

Principled methods with which to appropriately analyze missing data have long existed;
however, broad implementation of these methods remains challenging. In this and 2 …

Multivariate imputation by chained equations (MICE) has emerged as a principled method of
dealing with missing data. Despite properties that make MICE particularly useful for large …

Abstract Background and Objectives As a result of the development of sophisticated
techniques, such as multiple imputation, the interest in handling missing data in longitudinal …

Background Multiple imputation has become very popular as a general-purpose method for
handling missing data. The validity of multiple-imputation-based analyses relies on the use …

Statistical analysis in epidemiologic studies is often hindered by missing data, and multiple
imputation is increasingly being used to handle this problem. In a simulation study, the …

Epidemiologic studies are frequently susceptible to missing information. Omitting
observations with missing variables remains a common strategy in epidemiologic studies …

OBJECTIVE: Overweight and obesity are increasingly prevalent in the general pediatric
population. Evidence suggests that children with autism spectrum disorders (ASDs) may be …