Descriptive statistics after multiple imputation
How do you Analyse imputed data?
Analyzing multiply imputed data involves two steps: 1) running standard analyses (e.g., regression) on each of the imputed datasets, and 2) combining the estimates from each dataset to obtain the final result..
How do you describe multiple imputation?
Multiple imputation fills in missing values by generating plausible numbers derived from distributions of and relationships among observed variables in the data set..
What do you do after multiple imputations?
After Multiple Imputation has been performed, the next steps are to apply statistical tests in each imputed dataset and to pool the results to obtain summary estimates.
In SPSS and R these steps are mostly part of the same analysis step..
What is multiple imputation in simple terms?
Multiple imputation fills in missing values by generating plausible numbers derived from distributions of and relationships among observed variables in the data set..
- Multiple imputation is a general approach to the problem of missing data that is available in several commonly used statistical packages.
It aims to allow for the uncertainty about the missing data by creating several different plausible imputed data sets and appropriately combining results obtained from each of them. - The major danger of the technique is that it may provide nonsensical or even misleading results if applied without appropriate care or insight.
Multiple imputation is not a simple technical fix for the missing data.
Apr 20, 2015Hello Statalisters, I'm trying to obtain descriptive statistics for variables in an imputed dataset (100 imputations, using ice in STATA13).Multiple imputations and descriptive statistics - StatalistDescriptive statistics (such mean, sd, etc) of variables after multiple 2x2 tables with multiple imputed data - StatalistUsing svy and mi prefixes: How to complete a descriptive table on More results from www.statalist.org
How are descriptive statistics used in imputed data sets?
Descriptive statistics in the imputed data sets Nonparametric density plots were used to describe the distribution of the 4 continuous variables that were subject to missing data in the complete cases and in those subjects who were missing data for the given continuous variable
The latter was done separately in each of the imputed data sets
Is multiple imputation meant for descriptives?
The number of observations is an indicator that this is the case (even though you could end up with the same N but different cases)
Anyway, as I have stated above, I do not believe that multiple imputation was meant for descriptives
What is multiple imputation for continuous variables?
Multiple imputation for continuous variables with the use of predictive-mean matching The imputation process described above uses linear regression and takes the imputed values as random draws from a normal distribution
The typical sequence of steps to do a multiple imputation analysis is: Impute the missing data by the mice function, resulting in a multiple imputed data set (class mids); Fit the model of interest (scientific model) on each imputed data set by the with () function, resulting an object of class mira;imp.data <- complete (imp, "long") (means <- with (imp.data, tapply (bmi,.imp, mean))) mean (means) (variances <- with (imp.data, tapply (bmi,.imp, var))) mean (variances) If you want inference, you can manually apply Rubin's rules to the estimates.
