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Activity Number: 438 - Missing Data Issues in Public Health Studies and Survey Sampling in the Era of Data Science
Type: Invited
Date/Time: Wednesday, July 31, 2019 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Epidemiology
Abstract #300511 Presentation
Title: Multilevel Multiple Imputation for Electronic Health Record and Survey Data: Your Flexible Friend
Author(s): James Robert Carpenter* and Matteo Quartagno
Companies: London School of Hygiene & Tropcial Medicine and London School of Hygiene & Tropcial Medicine
Keywords: missing data; multiple imputation; multilevel

Multiple imputation is now well established as a practical and flexible method for analysing partially observed data under the missing at random assumption. However, in large datasets there are concerns about how to preserve heterogeneity in the relationship between variables in the imputation process.

Building on recent work, we describe an imputation model (and R software) which allows the covariance matrix of the variables to vary randomly across higher level units, which may represent health districts or hospitals.

We further show how this approach adapts to (i) impute data consistent with interaction and non-linear effects under investigation (ii) include weights, when the substantive model is weighted, and (iii) incorporate external information, when available.

We illustrate with an example from the UK Clinical Practice Research Datalink.

Authors who are presenting talks have a * after their name.

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