Three-level data is common in medical research, as is missing. While multiple imputation (MI) is widely used to handle missing data in such studies, its validity depends on the appropriate tailoring of the imputation model to the substantive analysis. This means all the key features of the substantive analysis such as non-linear relationships, interactions and multilevel features should be appropriately accommodated in the imputation process. This paper evaluates a number of MI approaches that may be used for imputing three-level data when the substantive analysis model contains interactions and non-linear terms using both a simulation and a case study.
The Missing Data, Imputation and Analysis (MiDIA) Group meets 4 times a year, to discuss work in progress in the area of missing data methodology. Its members come from LSHTM, University of Bristol, Murdoch Childrens Research Institute, MRC CTU and …
**Background**: Three-level data structures arising from repeated measures on individuals who are clustered within larger units are common in health research studies. Missing data are prominent in such studies and are often handled via multiple …