Phoenix can handle a wide variety of model designs suitable for assessing indi-vidual and population bioequivalence, including:
TRTR/RTRT/TRRT/RTTR TT/RR/TR/RT
TRT/RTR/TRR/RTT TRRTT/RTTRR TRR/RTR/RRT RTR/TRT
TRR/RTT/TRT/RTR/TTR/RRT TRRR/RTTT
TTRR/RRTT/TRRT/RTTR/TRRR/RTTT where T=Test formulation and R=Reference formulation.
Note: Each sequence must contain the same number of periods. For each period, each subject must have one measurement.
The Getting Started Guide shows results for a RTR/TRT design, which is recom-mended in the U.S. FDA individual and population bioequivalence guidelines.
This example demonstrates an analysis of a TT/RR/TR/RT design.
The population/individual model
Import the data set:
1. Select File > Import or click the Import button. The Open File(s) dialog is displayed.
2. Navigate to the Phoenix examples directory, which by default is located at C:\Program
Files\Pharsight\Phoenix\applica-tion\Examples.
3. Select TT RR RT TR.DAT and click Open.
The Worksheet Import Options dialog is displayed. The dialog is used to assign options for how the data are imported and presented.
4. Click Finish. The data set is added to the project’s Data folder.
A data set in DAT (ASCII data) format is added to the Data folder as a worksheet.
The model
Note that the number of subjects is not the same in each sequence group:
Begin bioequivalence:
1. Select the workflow in the Object Browser and then select Insert > NCA and Toolbox > Bioequivalence.
The Bioequivalence object is added to the workflow in the Object Browser.
2. Map the data set TT RR RT TR as the input source for the Bioequivalence 2 object:
• Use the pointer to drag the TT RR RT TR worksheet from the Data folder to the Main Mappings panel.
• Select the TT RR RT TR worksheet and click Select.
The TT RR RT TR data set is mapped to the Bioequivalence 2 object.
3. In the Model tab, select the Population/Individual option button in the Type of Bioequivalence area.
The mapping contexts in the Main Mappings panel are automatically updated.
4. Use the option buttons in the Main Mappings panel to map the data types to the following contexts:
• Map AUC to the Dependent context.
The following data types are automatically mapped to contexts when the data set is mapped to the Bioequivalence model. If they are not, use the option buttons in the Main Mappings panel to map the data types to the appropriate contexts.
• Sequence is mapped to the Sequence context.
• Subject is mapped to the Subject context.
• Period is mapped to the Period context.
• Formulation is mapped to the Formulation context.
Set up the model:
1. Use the Model tab to specify settings for Bioequivalence model options.
• Crossover is automatically selected in the Type of study area. Crossover studies are the only permitted type for Population/Individual bioequivalence analysis.
• Select Population/Individual as the Type of Bioequivalence.
• R is automatically selected in the Reference Value menu. Do not change this setting.
2. Select the Fixed Effects tab, which is located underneath the Setup tab.
• Ln(x) is automatically selected in the Dependent Variables Transforma-tion menu. Do not change this setting. The values will be log-transformed before the analysis.
3. Select the Options tab, which is located underneath the Setup tab.
4. In the Confidence Level field type 95 to set the confidence level to 95%.
The default bioequivalence options reflect the recommendations in the U.S. FDA (2001) guidelines on individual and population bioequivalence.
5. Click the Execute button. The results are displayed on the Results tab.
Results
Partial Population/Individual results worksheet:
Inspect the results for mixed scaling. For population bioequivalence the upper confidence limit is 0.014 > 0, and therefore population BE has not been shown.
For individual bioequivalence the upper confidence limit is –0.05 < 0, and so indi-vidual BE has been shown.
Comparing average bioequivalence
Re-analyze the data for average bioequivalence:
1. This example is the second part of the Individual and population bioequiva-lence section that starts on page page 174.
2. This example uses the data set TT RR RT TR from the The population/indi-vidual model example on page page 175.
3. Repeat steps 1. and 2. under Begin bioequivalence: on page 175 to insert
Statistic Value Upper_CI Conclusion
Differ-ence(Delta)
-0.013
Ratio(%Ref) 98.735 BE shown for ratio test
SigmaR 0.345
SigmaWR 0.05
Ref_Pop_eta -0.229 0.014 Pop. BE not shown for refnc-scaling CI test
Const_Pop_eta -0.091 0.101 Pop. BE not shown for const-scaling CI test
Mixed_Pop_eta -0.229 0.014 Pop. BE not shown for mixed-scaling CI test
Ref_Indiv_eta 0.001 0.043 Indiv. BE not shown for refnc-scaling CI test
Const_Indiv_et a
-0.093 -0.05 Indiv. BE shown for const-scaling CI test
Mixed_Indiv_et a
-0.093 -0.05 Indiv. BE shown for mixed-scaling CI test
Set up the model:
Use the Model tab to specify settings for Bioequivalence model options. The Model tab is located underneath the Setup tab.
1. Make sure that Crossover is selected as the Type of study, Average is selected as the Type of Bioequivalence, and R is selected as the Refer-ence Formulation.
2. Select the Fixed Effects tab, which is located underneath the Setup tab.
• Sequence+Formulation+Period is automatically selected as the default Model Specification. Do not change this setting.
• Ln(x) is automatically selected in the Dependent Variables Transforma-tion menu. Do not change this setting.
3. Select the Variance Structure tab, which is located underneath the Setup tab.
4. Select the Variance Structure’s Random 1 tab.
5. In the Random 1 tab, Formulation is in the Random Effects field. If not, drag Formulation from the Classification Variables list or type Formula-tion in the Random Effects field.
6. Subject is in the Variance Blocking Variables (Subject) field. If not, drag Subject from the Classification Variables list or type Subject in the Vari-ance Blocking Variables (Subject) field.
7. Make sure Banded No-Diagonal Factor Analytic(f) is selected in the Type menu.
8. Make sure 2 is In the Number of factors (f) = field. If it is not, type 2 in the field.
9. Select the Variance Structure’s Repeated tab.
10.Period is in the Repeated Specification field. If not, drag Period from the Classification Variables list or type Period in the Repeated Specification field.
11.Subject is in the Variance Blocking Variables (Subject) field. If not, drag Subject from the Classification Variables list or type Subject in the Vari-ance Blocking Variables (Subject) field.
12.Formulation is in the Group field. If not, drag Formulation from the Classi-fication Variables list or type Formulation in the Group field.
13.Click the Execute button. The results are displayed on the Results tab.
Using the FDA model for average bioequivalence on replicated crossover designs resulted in a 90% lower confidence interval of 87.277% and a 99.715%
upper confidence interval for the ratio of average AUC. Therefore a user can also
conclude average bioequivalence is achieved. This is not always the case. Data can pass individual BE and fail average BE, and data can also pass average BE and fail individual BE.
Note: It is not necessary to keep a project open after completing each chapter. This project is not required when working in the next chapter. To close a project right-click the project and select Close Project.