Transcription of Combining Analysis Results from Multiply Imputed ...
{{id}} {{{paragraph}}}
Example: air traffic controller
Combining Analysis Results from Multiply Imputed ...
1 PharmaSUG 2013 - Paper SP03 Combining Analysis Results from Multiply Imputed categorical data Bohdana Ratitch, Quintiles, Montreal, Quebec, Canada Ilya Lipkovich, Quintiles, NC, Michael O Kelly, Quintiles, Dublin, Ireland ABSTRACT Multiple imputation (MI) is a methodology for dealing with missing data that has been steadily gaining wide usage in clinical trials. Various methods have been developed and are readily available in SAS PROC MI for multiple imputation of both continuous and categorical variables. MI produces multiple copies of the original dataset, where missing data are filled in with values that differ slightly between Imputed datasets. Each of these datasets is then analyzed using a standard statistical method for complete data , and the Results from all Imputed datasets are combined (pooled) for overall inference using Rubin s rules which account for the uncertainty associated with Imputed values.
Combining Analysis Results from Multiply Imputed Categorical Data, continued 4 2. Analysis: each of the M imputed datasets is analyzed separately using any method that would have been chosen had the data been complete. This step can be implemented using any analytical procedure in SAS,
Tags:
Information
Domain:
Source:
Link to this page:
Please notify us if you found a problem with this document:
Related search queries
SPSS-Applications Data Analysis, Analysis, Survey Data, Linear Regression Analysis for Survey Data, Data analysis, Data, Data Analysis in SPSS Department of Psychology, Data Analysis in SPSS, Department of Psychology, Quantitative research, Logistic Regression Analysis, Logistic regression, Bian Office for Faculty Excellence