Pattern-mixture models for multivariate incomplete data with covariates

被引:150
作者
Little, RJA [1 ]
Wang, YX [1 ]
机构
[1] UNIV CALIF LOS ANGELES,DEPT PSYCHOL,LOS ANGELES,CA 90024
关键词
attrition; EM algorithm; Gibbs' sampling; informative drop-outs; missing values; nonrandom missing data; nonresponse; selection bias;
D O I
10.2307/2533148
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Pattern-mixture models stratify incomplete data by the pattern of missing values and formulate distinct models within each stratum. Pattern-mixture models are developed for analyzing a random sample on continuous variables y((1)), Y-(2) when values of y((2)) are nonrandomly missing. Methods for scalar y((1)) and y((2)) are here generalized to vector y((1)) and y((2)) with additional fixed covariates x. Parameters in these models are identified by alternative assumptions about the missing-data mechanism. Models may be underidentified (in which case additional assumptions are needed), just-identified, or overidentified. Maximum likelihood and Bayesian methods are developed for the latter two situations, using the EM and SEM algorithms, direct and iterative simulation methods. The methods are illustrated on a data set involving alternative dosage regimens for the treatment of schizophrenia using haloperidol and on a regression example. Sensitivity to alternative assumptions about the missing-data mechanism is assessed, and the new methods are compared with complete-case analysis and maximum likelihood for a probit selection model.
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页码:98 / 111
页数:14
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