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(2010)METHODOLOGY-EUROPEAN JOURNAL OF RESEARCH METHODS FOR THE BEHAVIORAL AND SOCIAL SCIENCES.6(1).p.37-48 Mark abstract This article reviews inverse probability weighting methods and doubly robust estimation methods for the analysis of incomplete data sets. We first consider methods for estimating a population mean when the outcome is missing at random, in the sense that measured covariates can explain whether or not the outcome is observed. We then sketch the rationale of these methods and elaborate on their usefulness in the presence of influential inverse weights. We finally outline how to apply these methods in a variety of settings, such as for fitting regression models with incomplete outcomes or covariates, emphasizing the use of standard software programs.

Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-1073694



Author: Stijn Vansteelandt , James Carpenter and Michael G Kenward

Source: https://biblio.ugent.be/publication/1073694



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