Multiple imputation for missing values through conditional Semiparametric odds ratio models.

ABSTRACT: Multiple imputation is a practically useful approach to handling incompletely observed data in statistical analysis. Parameter estimation and inference based on imputed full data have been made easy by Rubin's rule for result combination. However, creating proper imputation that accommodates flexible models for statistical analysis in practice can be very challenging. We propose an imputation framework that uses conditional semiparametric odds ratio models to impute the missing values. The proposed imputation framework is more flexible and robust than the imputation approach based on the normal model. It is a compatible framework in comparison to the approach based on fully conditionally specified models. The proposed algorithms for multiple imputation through the Markov chain Monte Carlo

SUBMITTER: Chen HY 

PROVIDER: S-EPMC3135790 | biostudies-literature | 2011 Sep

REPOSITORIES: biostudies-literature