Imputation is one of the most common methods to reduce item non_response effects. Imputation results in a complete data set, and then it is possible to use naϊve estimators. After using most of common imputation methods, mean and total (imputation estimators) are still unbiased. However their variances (imputation variances) are underestimated by naϊve variance estimators. Sampling mechanism an...

in interventional or observational longitudinal studies, the issue of missing values is one of the main concepts that should be investigated. the researcher's main concerns are the impact of missing data on the final results of the study and the appropriate methods that missing values should be handled. regarding the role and the scale of the variable that missing values have been occurred and ...

background: multifactorial regression models are frequently used in medicine to estimate survival rate of patients across risk groups. however, their results are not generalisable, if in the development of models assumptions required are not satisfied.  missing data is a common problem in pathology. the aim of this paper is to address the danger of exclusion of cases with missing data, and to h...

background: prognostic models have clinical appeal to aid therapeutic decision making. two main practical challenges in development of such models are assessment of validity of models and imputation of missing data. in this study, importance of imputation of missing data and application of bootstrap technique in development, simplification, and assessment of internal validity of a prognostic mo...

Due to the growing need to combine data across multiple studies and to impute untyped markers based on a reference sample, several analytical tools for imputation and analysis of missing genotypes have been developed. Current imputation methods rely on single imputation, which ignores the variation in estimation due to imputation. An alternative to single imputation is multiple imputation. In t...

Appropriate imputation inference requires both an unbiased imputation estimator and an unbiased variance estimator. The commonly used variance estimator, proposed by Rubin, can be biased when the imputation and analysis models are misspecified and/or incompatible. Robins and Wang proposed an alternative approach, which allows for such misspecification and incompatibility, but it is considerably...

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...