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Application of Multiple Imputation Method in Analyzing Data with Missing Continuous Covariates
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 Title & Authors
Application of Multiple Imputation Method in Analyzing Data with Missing Continuous Covariates
Ghasemizadeh Tamar, S.; Ganjali, M.;
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 Abstract
Missing continuous covariates are pervasive in the use of generalized linear models for medical data. Multiple imputation is the most common and easy-to-do method of dealing with missing covariate data. However, there are always serious warnings in using this method. There should be concern to make imputed values more proper. In this paper, proper imputation from posterior predictive distribution is developed for implementing with arbitrary priors. We use empirical distribution of the posterior for approximating the posterior predictive distribution, to sample from it. This method is preferable in comparison with a presented imputation method of us which uses a full model to impute missing values using available software. The proposed methods are implemented on glucocorticoid data.
 Keywords
Generalized linear model;missing data;proper multiple imputation;predictive distribution;full imputation model;
 Language
English
 Cited by
 References
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