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...

This report provides a review of the multiple imputation methodology used by the Sponsor (Acorn Cardiovascular, Inc.) to handle missing data in the analysis of the primary endpoint of its pivotal clinical trial of the CorCap Cardiac Support Device (CorCap), as well as an evaluation of the impact of multiple imputation on the results of that analysis. This included a completely blinded imputatio...

Political scientists increasingly recognize that multiple imputation represents a superior strategy for analyzing missing data to the widely used method of listwise deletion. However, there has been little systematic investigation of how multiple imputation affects existing empirical knowledge in the discipline. This article presents the first large-scale examination of the empirical effects of...

Finite sample properties of multiple imputation estimators under the linear regression model are studied. The exact bias of the multiple imputation variance estimator is presented. A method of reducing the bias is presented and simulation is used to make comparisons. We also show that the suggested method can be used for a general class of linear estimators. 1. Introduction. Multiple imputation...

The performance of multiple imputation in questionnaire data has been studied in various simulation studies. However, in practice, questionnaire data are usually more complex than simulated data. For example, items may be counterindicative or may have unacceptably low factor loadings on every subscale, or completely missing subscales may complicate computations. In this article, it was studied ...

Models for survival data generally assume that covariates are fully observed. However, in medical studies it is not uncommon for biomarkers to be censored at known detection limits. A computationally-efficient multiple imputation procedure for modeling survival data with covariates subject to detection limits is proposed. This procedure is developed in the context of an accelerated failure time...

The performance of five simple multiple imputation methods for dealing with missing data were compared. In addition, random imputation and multivariate normal imputation were used as lower and upper benchmark, respectively. Test data were simulated and item scores were deleted such that they were either missing completely at random, missing at random, or not missing at random. Cronbach's alpha,...