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ModelsAdjusting Imputed Values in Pattern-Mixture ModelsSummary of Issues in Multiple ImputationPlot Options Superseded by ODS GraphicsODS Table NamesODS Graphics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
Specifying Sets of Observations for Imputation in Pattern-Mixture Models
By default, all available observations are used to derive the imputation model. By using the MODEL option in the MNAR statement, you can specify the set of observations that are used to derive the model. You specify a classification variable (obs-variable) by using the option MODELOBS= (obs-variable= ’level1’ <’level2’ …>). The MI procedure uses the group of observations for which obs-variable equals one of the specified classification levels.
When you use the MNAR statement together with a MONOTONE statement, you can also use the MODELOBS=CCMV and MODELOBS=NCMV options
to specify the set of observations for deriving the imputation model. For a monotone missing pattern data set that contains
the variables 
, 
, …, 
(in that order), there are at most p groups of observations such that the same number of variables is observed for observations in each group. The complete-case
missing values (CCMV) method (Little 1993; Molenberghs and Kenward 2007, p. 35) uses the group of observations for which all variables are observed (complete cases) to derive the imputation model.
The neighboring-case missing values (NCMV) method (Molenberghs and Kenward 2007, pp. 35–36) uses only the neighboring group of observations (that is, for 
, the group of observations with observed and 
missing).
In PROC MI, the option MODELOBS=CCMV(K=k) uses the k groups of observations together with as many observed variables as possible to derive the imputation model. For instance,
specifying K=1 (which is the default) uses observations from the group that has all variables observed (complete cases). Specifying
K=2 uses observations from the two groups that have the most variables observed (the group of observations that has all variables
observed and the group of observations that has 
observed but missing).
For an imputed variable , the option MODELOBS=NCMV(K=k) uses the k closest groups of observations that have observed but have as few observed variables as possible to derive the imputation model. For instance, specifying K=1 (which is the
default) uses the group of observations that has observed but missing (neighboring cases). Specifying K=2 uses observations from the two closest groups that have observed (the group of observations that has observed but missing, and the group of observations that has observed and 
missing).
When you use the MNAR statement together with an FCS statement, the MODEL option applies only after the preliminary filled-in phase in each of the imputations.
For an illustration of the MODEL option, see Example 75.15.