Affiliation:
1. Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA; email: rlittle@umich.edu
Abstract
Methods for handling missing data in clinical psychology studies are reviewed. Missing data are defined, and a taxonomy of main approaches to analysis is presented, including complete-case and available-case analysis, weighting, maximum likelihood, Bayes, single and multiple imputation, and augmented inverse probability weighting. Missingness mechanisms, which play a key role in the performance of alternative methods, are defined. Approaches to robust inference, and to inference when the mechanism is potentially missing not at random, are discussed.
Reference93 articles.
1. Missing observations in multivariate statistics 1: review of the literature;J. Am. Stat. Assoc.,1966
2. Maximum likelihood estimates for the multivariate normal distribution when some observations are missing;J. Am. Stat. Assoc.,1957
3. Prior approval: the growth of Bayesian methods in psychology;Br. J. Math. Stat. Psychol.,2013
4. A review of hot deck imputation for survey nonresponse;Int. Stat. Rev.,2010
5. Proxy pattern-mixture analysis for survey nonresponse;J. Off. Stat.,2011
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