Skip to contents

Coordinate the bioequivalence calculation path

Source: R/bioequivalence.R
be_fit_models.Rd

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,
  covariates = NULL,
  heteroscedastic = FALSE
)

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", "HVNTID", or "descriptive" (no acceptance limits or pass/fail).

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), "nlme" (mixed model with treatment-specific residual variances, for crossover/replicate designs), or "gls" (generalized least squares with treatment-specific residual variances, for parallel designs). When NULL (default) it is chosen from the design, the regulator, and heteroscedastic.

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.

covariates

An optional character vector of column names added to every model as additive fixed effects (numeric columns as linear terms, character or factor columns as factors). They must not be missing in any analyzed row. For a PKNCAresults object they must be columns of as.data.frame(object), which means grouping columns. The within-subject variances used for reference scaling and the intra-subject contrasts do not use covariates.

heteroscedastic

Logical. When TRUE, estimate a separate residual variance for each treatment (see Details).

Value

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

See also

Other Bioequivalence: be_assess(), be_compare(), be_dataset(), be_design(), be_expand_limits(), be_extract_param(), be_fit_model_single(), be_regulator(), be_table(), be_within_var()