Multiple Imputation

PhD thesis

Doctor of Philosophy - Medicine, Dentistry and Health Sciences

Multiple imputation approaches for handling incomplete three-level data with time varying cluster memberships

Three-level data structures arising from repeated measures on individuals who are clustered within larger units are common in clinical and population health studies. An additional complexity arises when individuals move between clusters over the …

Evaluation of approaches for multiple imputation of three-level data

Multilevel data with three levels of hierarchy resulting from repeated measures data on individuals who are clustered within larger units, such as geographical region, are common in health research studies. Missing data, which is a pervasive problem …

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.

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

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 …

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

**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 …

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 …