title: Introduction to multiple imputation for dealing with missing data.
abstract: Missing data are common in both observational and experimental studies. Multiple imputation (MI) is a two-stage approach where missing values are imputed a number of times using a statistical model based on the available data and then inference is combined across the completed datasets. This approach is becoming increasingly popular for handling missing data. In this paper, we introduce the method of MI, as well as a discussion surrounding when MI can be a useful method for handling missing data and the drawbacks of this approach. We illustrate MI when exploring the association between current asthma status and forced expiratory volume in 1 s after adjustment for potential confounders using data from a population-based longitudinal cohort study.
authors.affiliation: Clinical Epidemiology and Biostatistics Unit, Murdoch Childrens Research Institute, Melbourne, Victoria, Australia.|Department of Paediatrics, The University of Melbourne, Melbourne, Victoria, Australia.
authors.forename: Katherine J
authors.initials: KJ
authors.lastname: Lee
authors.affiliation: Centre for Molecular, Environmental, Genetic & Analytic Epidemiology, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia.
authors.forename: Julie A
authors.initials: JA
authors.lastname: Simpson
mesh_terms: D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical; D017063:Outcome Assessment, Health Care; D012107:Research Design / Q000706:statistics & numerical data*
keywords: experimental study; missing data; multiple imputation; observational study