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be_fit_models() runs the single bioequivalence calculation path and returns the regulatory pass/fail table. It coordinates the stages in order: be_dataset() to prepare the data, be_design() to classify the design and choose the model, be_fit_model_single() to fit, be_extract_param() to extract the parameters, and be_table() to apply the regulatory decision. be_assess() and be_compare() are thin verbs over it.

Usage

be_fit_models(
  object,
  reference_col,
  reference_value,
  endpoints = c("cmax", "aucinf.obs", "aucinf.pred", "auclast"),
  regulator = "ABE",
  model_type = NULL,
  alpha = 0.1,
  subject = NULL,
  sequence = NULL,
  period = NULL,
  design = NULL
)

Arguments

object

A PKNCAresults object or a tidy long data.frame with a PPTESTCD column of parameter names, a PPORRES/PPSTRES column of values, and subject/sequence/period/treatment columns.

reference_col

The column identifying the formulation/treatment.

reference_value

The value of reference_col that is the reference formulation.

endpoints

Character vector of NCA parameters (matched against PPTESTCD) to assess.

regulator

The regulatory framework (see be_regulator()); one of "ABE", "EMA", "HC", "GCC", "FDA", "NTID", or "HVNTID".

model_type

The model for the average-BE point estimate, one of "lmer" (mixed model, for crossover/replicate designs), "anova" (fixed-effects, for parallel designs), "isc" (intra-subject contrasts, the FDA reference-scaled path), or "nlme" (treatment-specific mixed model). When NULL (default) it is chosen from the design and regulator.

alpha

The significance level; the confidence interval has level 1 - alpha (default 0.10 gives the 90% interval).

subject, sequence, period

Column names for the subject, randomization sequence, and period. When NULL they are taken from the PKNCAresults object or detected from common column names. sequence may be absent.

design

An optional be_design() object; computed from the data when NULL.

Value

A data.frame with one row per endpoint and test formulation (the columns described in be_assess()).