Covariate modeling with the PK01 dataset
The data used in the PK01 dataset is from a simulated trial. The purpose of this example is to show how to add covariate effects to a model to determine which covariates have an effect on the model’s results.
Note:The completed project (PK01Co3.phxproj) is available for reference in …\Examples\NLME.
Set up the Maximum Likelihood Models object
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Create a new project called PK01Co3.
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Import the dataset …\Examples\NLME\Supporting files\pk01cov3.dat.
Click Finish in the File Import Wizard dialog. -
Right-click the worksheet and select Send To > Modeling > Maximum Likelihood Models.
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Rename the object Base Model.
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Select the option buttons in the Main Mappings panel to map the data types to the following contexts:
id to the ID context.
time to the Time context.
conc to the CObs context.
dose to the A1 context.
Leave all other data types mapped to None. -
In the Structure tab, make sure Clearance is selected in the Parameterization menu.
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In the C (Continuous Observation) error model menu, select Multiplicative.
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Select the Parameters > Fixed Effects sub-tab.
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In the Initial column, type the following initial estimates for each of the study parameters:
tvV 10
tvCl 0.1
Add covariates
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Select the Structural sub-tab.
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Click Add From Unused.
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Select the checkboxes beside sex, wt, and age.
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Click Add.
The covariate data types are automatically mapped to the correct contexts. -
Select the Covar. Type sub-tab.
Sex is automatically selected in the Select covariate menu. -
In the Type menu, select Type: Categorical.
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Check the Allow arbitrary category names checkbox.
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In the first field underneath Category Name, type male.
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Leave 0 as the value for male.
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In the second field underneath Category Name, type female.
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Type 1 in the value field for female.
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Click
(Execute icon) to execute the object.
Add covariate effects for weight and age
In this model the covariate effects for weight and age are added to the study parameters.
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Right-click Base Model and select Copy.
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Right-click the Workflow object and select Paste.
The model object and its settings are pasted into the PK01Co3 model project and named Copy of Base Model. -
Rename Copy of Base Model to Covariate Model.
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In the Main Mappings panel, check that the data types are mapped as follows:
id to the ID context.
time to the Time context.
conc to the CObs context.
dose to the A1 context.
sex, wt, and age to sex, wt, and age, respectively. -
Select the Parameters > Structural sub-tab.
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In the V column, click No beside wt to select a covariate effect.
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In the Cl column, click No beside wt to select a covariate effect.
The study variable weight is added as a covariate effect to both V and Cl. -
In the Center field for wt type 75.
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In the Cl column, click No beside age to select a covariate effect.
The study variable age is added as a covariate effect to Cl. -
In the Center field for age type 40.
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Select the Fixed Effects sub-tab.
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Click Accept All Fixed+Random to copy the new estimates to the Initial estimates field for each parameter.
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In the Initial column, leave the initial values for dVdwt, dCldwt, and dCldage set to 0 (zero).
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Execute the object.
Add sex as a covariate effect
In this model the covariate effect for sex is added to the study parameter volume.
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Right-click Covariate Model and select Copy.
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Right-click the Workflow object and select Paste.
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Rename the copied object as Covariate Model_sexcov.
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In the Main Mappings panel, check that the data types are mapped as follows:
id to the ID context.
time to the Time context.
conc to the CObs context.
dose to the A1 context.
sex, wt, and age to sex, wt, and age, respectively. -
Select the Parameters > Structural sub-tab.
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In the V column, click No beside the sex parameter.
The study variable sex is added as a covariate effect to V. -
Select the Fixed Effects sub-tab.
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Click Accept All Fixed+Random to copy the new estimates to the Initial estimates field for each parameter.
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In the Initial column, type 1 for the initial estimate for the parameter dVdsex1.
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Execute the object.
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In the Results tab, select the Core Output text file.
Note that adding sex as a covariate has no effect on the fixed effects values, so the covariate can be removed from the model.
The concludes the covariate modeling example using the PK01 dataset.
