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Missing Data Project
| Topic | Leader |
Status | Most Recent Updated Time |
|---|---|---|---|
| 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 several methods to impute a missing value when a prediction rule is implemented in daily clinical practice. Two predictions rules are used: a diagnostic simplified score and a prognostic rule. The imputation methods include imputation of the mean (or median), the subgroup mean (or median), the use of the rule without the predictor with the missing values (by using the one-step-sweep method) and out of sample multiple imputation (the derivation data is stored and used to multiple impute the missing value). | Kristel | working | Dec 22,2006 |
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Topic revision: r6 - 22 Dec 2006, KristelJanssen
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