Three-level Data

PhD completion seminar: Evaluation of multiple imputation approaches for handling incomplete three-level data

**The event**:The completion seminar is an important milestone of PhD candidature where objectives, methods, findings and significance of the research conducted are presented in a public seminar with the aim of receiving constructive feedback from an …

Evaluation of approaches for accommodating interactions and non-linear terms in multiple imputation of incomplete three-level data

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.

Evaluation of approaches for multiple imputation of three-level data

While many MI approaches for imputing multilevel data have been developed recently, there is a lack of approaches specifically for three-level data and as a result, insufficient guidance for practitioners in the context of complex three-level data.This study evaluates the performance of available MI approaches for handling three-level incomplete data under a number of different scenarios via simulations and an empirical application based on a case study.

3MT (Three-Minute Thesis)

The 3 Minute Thesis (3MT) is an exercise in developing academic and research communication skills by reducing complex research down to a brief, interesting and accessible presentation. Competitors are challenged to present a clear and concise, yet …

Evaluation of approaches for multiple imputation in three-level data structures

Multilevel data with three levels of hierarchy are common in health research studies, for example when there are repeated measures (longitudinal) data from individuals who are further clustered within larger units. A common problem in such studies is …

Multiple imputation in three-level data structures

Multilevel data with three levels of hierarchy are common in health research studies. A common problem in such studies is the presence of missing data and often handled with multiple Imputation (MI). To our knowledge there are only two MI …