The MI Procedure
- Overview
- Getting Started
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Syntax
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DetailsDescriptive StatisticsEM Algorithm for Data with Missing ValuesStatistical Assumptions for Multiple ImputationMissing Data PatternsImputation MethodsMonotone Methods for Data Sets with Monotone Missing PatternsMonotone and FCS Regression MethodsMonotone and FCS Predictive Mean Matching MethodsMonotone and FCS Discriminant Function MethodsMonotone and FCS Logistic Regression MethodsMonotone Propensity Score MethodFCS Methods for Data Sets with Arbitrary Missing PatternsChecking Convergence in FCS MethodsMCMC Method for Arbitrary Missing Multivariate Normal DataProducing Monotone Missingness with the MCMC MethodMCMC Method SpecificationsChecking Convergence in MCMCInput Data SetsOutput Data SetsCombining Inferences from Multiply Imputed Data SetsMultiple Imputation EfficiencyNumber of ImputationsImputer’s Model Versus Analyst’s ModelParameter Simulation versus Multiple ImputationSensitivity Analysis for the MAR AssumptionMultiple Imputation with Pattern-Mixture ModelsSpecifying Sets of Observations for Imputation in Pattern-Mixture ModelsAdjusting Imputed Values in Pattern-Mixture ModelsSummary of Issues in Multiple ImputationPlot Options Superseded by ODS GraphicsODS Table NamesODS Graphics
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ExamplesEM Algorithm for MLEMonotone Propensity Score MethodMonotone Regression MethodMonotone Logistic Regression Method for CLASS VariablesMonotone Discriminant Function Method for CLASS VariablesFCS Methods for Continuous VariablesFCS Method for CLASS VariablesFCS Method with Trace PlotMCMC MethodProducing Monotone Missingness with MCMCChecking Convergence in MCMCSaving and Using Parameters for MCMCTransforming to NormalityMultistage ImputationCreating Control-Based Pattern Imputation in Sensitivity AnalysisAdjusting Imputed Continuous Values in Sensitivity AnalysisAdjusting Imputed Classification Levels in Sensitivity AnalysisAdjusting Imputed Values with Parameters in a Data Set
- References
Missing Data Patterns
The MI procedure sorts the data into groups based on whether the analysis variables are observed or missing. Note that the input data set does not need to be sorted in any order.
For example, with variables 
, 
, and 
(in that order) in a data set, up to eight groups of observations can be formed from the data set. Figure 75.6 displays the eight groups of observations and an unique missing pattern for each group.
Figure 75.6: Missing Data Patterns
An "X" in Figure 75.6 means that the variable is observed in the corresponding group and a "." means that the variable is missing. The MI procedure denotes variables that have missing values by "." or "O". The value "." means that the variable is missing and will be imputed, and the value "O" means that the variable is missing and will not be imputed.
The variable order is used to derive the order of the groups from the data set, and thus determines the order of missing values in the data to be imputed. If you specify a different order of variables in the VAR statement, then the results are different even if the other specifications remain the same.
A data set with variables , , …, 
(in that order) is said to have a monotone missing pattern
when the event that a variable 
is missing for a particular individual implies that all subsequent variables 
, 
, are missing for that individual. Alternatively, when a variable is observed for a particular individual, it is assumed that all previous variables , 
, are also observed for that individual.
For example, Figure 75.7 displays a data set of three variables with a monotone missing pattern.
Figure 75.7: Monotone Missing Patterns
Figure 75.8 displays a data set of three variables with a non-monotone missing pattern.
Figure 75.8: Non-monotone Missing Patterns
A data set with an arbitrary missing pattern is a data set with either a monotone missing pattern or a non-monotone missing pattern.