Topic | Leader | Status | Most Recent Updated Time |
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Compare MICE, argeimpute, and/or probably some maximum likelihood methods for normal distribution up to 99% missing |
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Illustration of situations where prediction mean matching may fail,and to develop a diagnostic how a researcher can detect a priori that he/she is in such situation |
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Study the situation when there are multiple covariates and/or outcome missing simultaneously, and check how bad it can get |
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Study categorical missing covariates using Fisher's optimum scoring algorithm, and study how good the prediction mean match based on canonical variable scores is comparing to polytomous logistic full model or MICE |
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Over-fitting (or over-imputing) issue | |||
Compare CC, MICE, and dropping missing variable for diagnostic data. There will be one continuous variable and two binary variables in two settings: (a) continous variable is missing (b)one of the binary variables is missing The missing percentage varies from 10% to 90%. |
Kristel | working | Nov 27,2006 |
Compare CC, MICE, and dropping missing variable for etiology data. There will be one continuous variable and two binary variables in two settings: (a) continous variable is missing (b)one of the binary variables is missing The missing percentage varies from 10% to 90%. |
Kristel | working | Nov 27,2006 |