The Fixed Effects tab allows users to specify settings for study variables used in an average bioequivalence model. Population and individual bioequivalence models do not use fixed effects, so most options in the Fixed Effects tab are unavailable for population or individual bioequivalence models.
Average bioequivalence
For average bioequivalence models the Model Specification field automatically displays an appropriate fixed effects model for the study type. Edit the model as needed.
Phoenix automatically specifies average bioequivalence models based on the study type selected and the dataset used. These default models are based on US FDA Guidance for Industry (January 2001).
See the following for details on the models used in a particular study type:
Replicated crossover designs
Nonreplicated crossover designs
Parallel designs
Study variables in the Classification box and the Regressors/Covariates box can be dragged to the Model Specification field to create the model structure.
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Drag variables from the Classification and the Regressors/Covariates boxes to the Model Specification field and click the operator buttons to build the model or type the names and operators directly in the field.
+ addition,
* multiplication,
( ) parentheses for indicating nested variables in the model
Below are some guidelines for using parentheses:
•Parentheses in the model specification represent nesting of model terms.
•Seq+Subject(Seq)+Period+Form is a valid use of parentheses and indicates that Subject is nested within Seq.
•Drug+Disease+(Drug*Disease) is not a valid use of parentheses in the model specification.
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Select a weight variable from the Regressors/Covariates box and drag it to the Weight Variable field.
To remove the weight variable, drag the variable from the Weight Variable field back to the Regressors/Covariates box.
The Regressors/Covariates box lists variables mapped to the Regressors context (in the Main Mappings panel). If a variable is used to weight the data then the variable is displayed in the Regressors/Covariates box. Below are some guidelines for using weight variables:
•The weights for each record must be included in a separate column in the dataset.
•Weight variables are used to compensate for observations having different variances.
•When a weight variable is specified, each row of data is multiplied by the square root of the corresponding weight.
•Weight variable values should be proportional to the reciprocals of the variances. Typically, the data are averages and weights are sample sizes associated with the averages.
•The Weight variable cannot be a classification variable. It must be declared as a regressor/covariate before it can be used as a weight variable. It can also be used in the model.
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In the Dependent Variables Transformation menu, select one of the transformation options:
None
Ln(x): Natural logarithmic transformation
Log10(x): Logarithmic base 10 transformation
Already Ln-transformed: Select if the dependent variable values are already transformed.
Already Log10-transformed: Select if the dependent variable values are already transformed.
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In the Fixed Effects Confidence Level box, type the level for the fixed effects model. The default value is 95%.
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By default, the intercept term is included in the model (although it is not shown in the Model Specification field), check the No Intercept checkbox to remove the intercept term.
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Use the Test Numerator and Test Denominator fields to specify an additional test of hypothesis in the case of a model with only fixed effects.
For this case, the default error term (denominator) is the residual error, so an alternate test can be requested by entering the fixed effects model terms to use for the numerator and denominator of the F-test. The terms entered must be in the fixed effects model and the random/repeated models must be empty for the test to be performed. (See “Tests of hypotheses” for additional information.) -
In the Dependent Variables Transformation menu, select Ln(x) or Already Ln-transformed.
Ln(x): Linear transformation
Already Ln-transformed: Select if the dependent variable values are already transformed.
