Background Three-level data arising from repeated measures on individuals who are
clustered within larger units are common in health research studies. Missing data are …

The treatment of missing data can be difficult in multilevel research because state-of-the-art
procedures such as multiple imputation (MI) may require advanced statistical knowledge or …

Principled techniques for incomplete data problems are increasingly part of mainstream
statistical practice. Among many proposed techniques so far, inference by multiple …

Background Multiple imputation (MI) is an established technique for handling missing data
in observational studies. Joint modelling (JM) and fully conditional specification (FCS) are …

Multiple imputation is becoming increasingly established as the leading practical approach
to modelling partially observed data, under the assumption that the data are missing at …

Multiple imputation (MI) is increasingly popular for handling multivariate missing data. Two
general approaches are available in standard computer packages: MI based on the …

Multiple Imputation is a flexible tool to handle missing data that has been increasingly used
in recent years. One of the conditions for its validity is that the two models used for (i) …

Multiple imputation (MI), a two-stage process whereby missing data are imputed multiple
times and the resulting estimates of the parameter (s) of interest are combined across the …

Missing data is a common problem in medical research, and is commonly addressed using
multiple imputation. Although traditional imputation methods allow for valid statistical …

Multiple imputation (MI) has emerged in the last two decades as a frequently used approach
in dealing with incomplete data. Gaussian and log‐linear imputation models are fairly …