Multiple-Imputation Inferences with Uncongenial Sources of Input

When Are Inferences from Multiple Imputation Valid?

Multiple imputation, as described by Rubin, has seen a wide variety of applications. Counterexamples, presented by Fay (1991), and new methods, such as those of J.N.K. Rao and J. Shao, that can asymptotically disagree with the multiple imputation approach, have raised questions about the validity of multiple imputation. This paper identifies critical restrictions on the practical application of...

Inferences for Two-Stage Multiple Imputation for Nonresponse

Multiple imputation is a common approach for handling missing data. It allows users to make valid inferences using standard complete-data methods with simple combining rules. A variation is to partition the missing data into two portions and conduct the imputation in two stages. We review two-stage multiple imputation and existing inferential methods and derive an alternative reference F -distr...

Multiple Imputation with Mplus

Multiple imputation: dealing with missing data.

In many fields, including the field of nephrology, missing data are unfortunately an unavoidable problem in clinical/epidemiological research. The most common methods for dealing with missing data are complete case analysis-excluding patients with missing data--mean substitution--replacing missing values of a variable with the average of known values for that variable-and last observation carri...

Chi-Square Statistics with Multiple Imputation