What does MI mean in Stata?

What does MI mean in Stata?

Stata has a suite of multiple imputation (mi) commands to help users not only impute their data but also explore the patterns of missingness present in the data. In order to use these commands the dataset in memory must be declared or mi set as “mi” dataset. A dataset that is mi set is given an mi style.

What is Mi estimate?

mi estimate estimates model parameters from multiply imputed data and adjusts coefficients and. standard errors for the variability between imputations. It runs the specified estimation command on. each of the M imputed datasets to obtain the M completed-data estimates of coefficients and their. VCEs.

What variables are included in multiple imputation?

The imputation model should always include all the variables in the analysis model, including the dependent variable of the analytic model as well as any other variables that may provide information about the probability of missigness, or about the true value of the missing data.

Why are auxiliary variables useful?

Auxiliary variables are used to optimize the sample, or to compile detailed tabulation when a frame is used for producing statistics directly, or to enhance other processes like editing and imputation.

Why is multiple imputation used?

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.

What is stochastic imputation?

In stochastic regression imputation, the noise is simulated by drawing random values from the residuals of the estimated regression model for each missing value and subsequently add them to the predicted missing value.

What is multiple imputation analysis?

Multiple imputation (MI) is a way to deal with nonresponse bias — missing research data that happens when people fail to respond to a survey. The technique allows you to analyze incomplete data with regular data analysis tools like a t-test or ANOVA.

Does multiple imputation induce bias?

Using Multiple Imputation to Avoid Bias From Missing Data in Critical Care Research. Missing data is a common, yet often overlooked, source of bias in critical care studies. Multiple imputation (MI) is a powerful alternative to complete case analysis that has several advantages.

What is complete case analysis?

Complete case analysis is the term used to describe a statistical analysis that only includes participants for which we have no missing data on the variables of interest. Participants with any missing data are excluded.