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- W2015661624 abstract "SUMMARY We propose a method for estimating parameters in generalized linear models when the outcome variable is missing for some subjects and the missing data mechanism is non-ignorable. We assume throughout that the covariates are fully observed. One possible method for estimating the parameters is maximum likelihood with a non-ignorable missing data model. However, caution must be used when fitting non-ignorable missing data models because certain parameters may be inestimable for some models. Instead of fitting a non-ignorable model, we propose the use of auxiliary information in a likelihood approach to reduce the bias, without having to specify a non-ignorable model. The method is applied to a mental health study." @default.
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- W2015661624 date "2001-09-01" @default.
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- W2015661624 title "Using Auxiliary Data for Parameter Estimation with Non-Ignorably Missing Outcomes" @default.
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- W2015661624 doi "https://doi.org/10.1111/1467-9876.00240" @default.
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