Bioequivalence user interface description
The Bioequivalence model object is based on a mixed effects model. For more see the “General linear mixed effects model” section.
Mappings panels identify how input variables are to be used. Map the data by dragging the dataset from the Data folder to the Setup tab or use the 
icon in the Setup tab.
Once a dataset is mapped, use the option buttons in the Mappings panels to assign the columns in the dataset to the appropriate context associations. Required context mappings are colored orange.
None: Columns mapped to this context are not included in any analysis or output.
Sort: Categorical variable(s) identifying individual data profiles.
Subject: The subjects in a dataset.
Sequence: The order of drug administration.
Period: The washout period, or the time period between two treatments needed for drug elimination. Only applicable in a crossover study.
Formulation: The treatment and reference drug formulations used in a study.
Dependent: The dependent variables, such as drug concentration, that provides the values used to fit the model.
Classification: Classification variables or factors that are categorical independent variables, such as formulation, treatment, and gender.
Regressors: Regressor variables or covariates that are continuous independent variables, such as gender or body weight. The regressor variable can also be used to weight the dataset.
Input data considerations
Missing data: For population and individual bioequivalence, the application assumes complete data for each subject. If a subject has a missing observation, that subject is not included in the analysis. If the data have many missing observations, consider imputing estimated values to produce complete records per subject. Phoenix does not impute missing values.
Variable name and data limits: See the “Data limits and constraints” section.