Predictive Model Based Multiple Imputation

This data set contains the following 11 variables measured for 50 patients in a clinical trial:

• OBS – Observation number.
• SYMPDUR – Duration of symptoms.
• AGE – The patient’s age.
• MeasA_0, MeasA_1, MeasA_2, andMeasA_3. The baseline measurement for the response variable MeasA and three post-baseline measurements taken at month 1, month 2, and month 3.
• MeasB_0, MeasB_1, MeasB_2, andMeasB_3. The baseline measurement for the response variable MeasB and three post-baseline measurements taken at month 1, month 2, and month 3.
• The variables OBS, SYMPDUR, AGE,MeasA_0, and MeasB_0 are all fully observed, and the remaining 6 variables contain missing values. To view the missing pattern for this data set, do the following:

1. From the datasheet window, selectView and Missing Data Pattern… In the Specify Missing Data Pattern window, press the Use All button.

2. From the View menu of the Missing Data Pattern window, select View Monotone Pattern to display the window shown on the right.

Note that after sorting the data into a Monotone pattern, the time structure of the longitudinal measures is preserved, so the missing data pattern in this data set is Monotone over time.

3. To close the Missing Data Patternwindow, select File and Close.

We will now multiply impute all of the missing values in this data set using the Predictive Model Based Method by executing the following steps:

1. From the Analyze menu, selectMultiple Imputation and Predictive Model Based Method.

2. The Specify Predictive Model window is displayed. The window opens with two pages or tabs: Base Setup and Advanced Options. As soon as you select a variable to be imputed, a Non-Monotone tab and a Monotone tab are also displayed.

Base Setup

 
Selecting the Base setup tab allows you specify which variables you want to impute, and which variables you want to use as covariates for the predictive model.

1. Using the datasheet MI_TRIAL, drag-and-drop the variables MeasA_1, MeasA_2, MeasA_3, MeasB_1, MeasB_2, MeasB_3 into the Variables to Impute field.
2. Drag and drop the variables SYMPDUR, AGE, MeasA_0, and MeasB_0 into the Fixed Covariates field.
3. As there is no Grouping variable in this data set, we can leave this field blank.

Selecting the Non-monotone tab allows you to add or remove covariates from the predictive model used for imputing the non-monotone missing values in the data set. (These can be identified in the Missing Data Pattern mentioned earlier.)

You select the + or - signs to expand or contract the list of covariates for each imputation variable.

For each imputation variable, the list of covariates will be made up of the variables specified as Fixed Covariates in the Base Setup tab, and all of the other imputation variables. Variables can be added and removed from this list of covariates by simply dragging and dropping the variable from the covariate list to the variables field, or vice versa. Even though a variable appears in the list of covariates for a particular imputation variable, it might not be used in the final model.

The program first sorts the variables so that the missing data pattern is as close as possible to monotone, and then, for each missing value in the imputation variable, the program works out which variables, from the total list of covariates, can be used for prediction.

By default, all of the covariates are forced into the model. If you uncheck a covariate, it will not be forced into the model, but will be retained as a possible covariate in the stepwise selection. Details of the models that were actually used to impute the missing values are included in the Output Log that can be selected from the View menu of the Multiply-Imputed Data Pages. These data pages will be displayed after you have specified the imputation and pressed the OK button in the Specify Predictive Model window.

Monotone

Selecting the Monotone tab allows you to add or remove covariates from the predictive model used for imputing the monotone missing values in the data set. (These can be identified in the Missing Data Pattern mentioned earlier.)

Again, you select the + or - signs to expand or contract the list of covariates for each imputation variable.

For each imputation variable, the list of covariates will be made up of the variables specified as Fixed Covariates in the Base Setup tab, and all of the other imputation variables. Variables can be added and removed from this list by simply dragging and dropping. Even though a variable appears in the list of covariates for a particular imputation variable, it might not be used in the final model. The program first sorts the variables so that the missing data pattern is as close as possible to monotone, and then uses only the variables that are to the left of the imputation variable as covariates. Details of the models that were actually used to impute the missing values are included in the Output Log.

Selecting the Advanced Options tab displays a window that allows you to choose control settings for the regression/discriminant model.

Randomization
Main Seed Value
The Main Seed Value is used to perform the random selection within the propensity subsets. The default seed is 12345. If you set this field to blank, or set it to zero, then the clock time is used.

Output Log
The Output Log is a comprehensive list of regression equations etc. that have been calculated for the imputed variable(s).

Least Squares Regression
Tolerance
The value set in the Tolerance datafield controls numerical accuracy. The tolerance limit is used for matrix inversion to guard against singularity. No independent variable is used whose R2 with other independent variables exceeds (1-Tolerance). You can adjust the tolerance using the scrolled datafield.

Stepping Criteria
Here you can select F-to-Enter and F-to-Remove values from the scrolled datafields, or enter your chosen value. If you wish to see more variables entered in the model, set the F-to-Enter value to a smaller value. The numerical value of F-to-remove should be chosen to be less than the F-to-Enter value.

When you are satisfied that you have specified your analysis correctly, click the OK button. The multiply-imputed datapages will

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