Advantages and Advancements of Multiple Imputation

Proper and improper multiple imputation

Multiple imputation has become viewed as a general solution to missing data problems in statistics. However, in order to lead to consistent asymptotically normal estimators, correct variance estimators and valid tests, the imputations must be proper. So far it seems that only Bayesian multiple imputation, i.e. using a Bayesian predictive distribution to generate the imputations, or approximatel...

Therapeutic Advancements in Multiple Myeloma

Multiple myeloma survival has significantly improved in the latest years due to a broad spectrum of novel agents available for treatment. The introduction of thalidomide, bortezomib, and lenalidomide together with autologous stem-cell transplantation has considerably increased complete remission rate and progression-free survival resulting ultimately in prolonged survival in myeloma patients. M...

Multiple Imputation with Mplus

Model - Based Multiple Imputation

managing Multiple imputation can improve data quality.

Local Multiple Imputation

Dealing with missing data via parametric multiple imputation methods usually implies stating several strong assumptions about both the distribution of the data and about underlying regression relationships. If such parametric assumptions do not hold, the multiply imputed data are not appropriate and might produce inconsistent estimators and thus misleading results. In this paper, a fully nonpar...