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pattern-mixture-model
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This project demonstrates that inaccurate conclusions may be drawn from partially observed data and proposes a strategy for mitigating such conclusions.
missing-datalatent-variable-modelsstructural-equation-modelsmissing-not-at-randompattern-mixture-model
- Updated
Apr 16, 2022 - R
R package for controlled multiple imputation of ordinal or binary responses with missing data in clinical study
statisticsbayesianmissing-dataglmr-packagemcmcjagsmultiple-imputationgeneralized-linear-modelsordinal-regressionreference-basedmissing-not-at-randommissing-at-randompattern-mixture-modelnon-ignorablecontrol-basedjump-to-referencecopy-referencedelta-adjustment
- Updated
Feb 18, 2023 - HTML
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