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
PKNCAresultsobject or a tidy long data.frame with aPPTESTCDcolumn of parameter names, aPPORRES/PPSTREScolumn of values, and subject/sequence/period/treatment columns.- reference_col
The column identifying the formulation/treatment.
- reference_value
The value of
reference_colthat 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). WhenNULL(default) it is chosen from the design and regulator.- alpha
The significance level; the confidence interval has level
1 - alpha(default0.10gives the 90% interval).- subject, sequence, period
Column names for the subject, randomization sequence, and period. When
NULLthey are taken from thePKNCAresultsobject or detected from common column names.sequencemay be absent.- design
An optional
be_design()object; computed from the data whenNULL.
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()