title: Multiple imputation: a primer.
abstract: In recent years, multiple imputation has emerged as a convenient and flexible paradigm for analysing data with missing values. Essential features of multiple imputation are reviewed, with answers to frequently asked questions about using the method in practice.
authors.affiliation: Department of Statistics, Pennsylvania State University, University Park 16802-6202, USA. jls@stat.psu.edu
authors.forename: J L
authors.initials: JL
authors.lastname: Schafer
mesh_terms: D001499:Bayes Theorem; D003627:Data Interpretation, Statistical; D016208:Databases, Factual / Q000706:statistics & numerical data; D004812:Epidemiologic Methods; D006302:Health Services Research / Q000706:statistics & numerical data; D015233:Models, Statistical; D009010:Monte Carlo Method