文章基本信息

标题：Missing data in regression models for non-commensurate multiple outcomes
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作者：Armando Teixeira-Pinto ; Sharon-Lise Normand.
期刊名称：RevStat : Statistical Journal
印刷版ISSN：1645-6726
出版年度：2011
卷号：9
期号：1
出版社：Instituto Nacional de Estatística
摘要：Biomedical research often involves the measurement of multiple outcomes in different scales (continuous, binary and ordinal). A common approach for the analysis of such data is to ignore the potential correlation among the outcomes and model each outcome separately. This can lead not only to loss of efficiency but also to biased estimates in the presence of missing data. We address the problem of missing data in the context of multiple non-commensurate outcomes. The consequences of missing data when using likelihood and quasi-likelihood methods are described, and an extension of these methods to the situation of missing observations in the outcomes is proposed. Two real data examples illustrate the methodology.
关键词：mixed outcomes; multivariate; latent variable; non-commensurate; missing data; maximum likelihood; direct maximization; weighted generalized estimating equations.