Assess bioequivalence against a regulatory framework

be_assess() performs a complete bioequivalence (BE) assessment of one or more noncompartmental endpoints against a regulatory framework, including reference scaling and the pass/fail decision. It accepts the results of pk.nca() directly (a PKNCAresults object) or a tidy long data.frame, so the same workflow that produces NCA parameters flows into the regulatory decision. It is a thin wrapper over be_fit_models() that adds the be_assess class and its print/summary methods.

Usage

be_assess(
  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

An object of class be_assess (a data.frame), ordered by endpoint (in the order requested) then by test formulation (reference-first), with one row per endpoint and test formulation and the columns endpoint, test, n, design, units (the endpoint's measurement units; the column is omitted with a warning when units are not provided), the reference and test geometric means with their 90% confidence intervals on the measurement scale (gm_reference, gm_reference_lower, gm_reference_upper, gm_test, gm_test_lower, gm_test_upper), gmr_percent, ci_lower, ci_upper, cvwr_percent, cvwt_percent, swr, limit_lower, limit_upper, criterion, regulator, model_type, and pass. limit_* are NA for the RSABE criterion and criterion is NA for the limit-based frameworks. A caption attribute documents the model and the decision rule.

Details

The model type is selected automatically from the design and the regulator. By design, a parallel study (one measurement per subject) uses a fixed-effects ANOVA ("anova"), while a crossover or replicate study (repeated measures per subject) uses the mixed model ("lmer") for the geometric mean ratio and its confidence interval. The FDA reference-scaled frameworks (FDA RSABE, NTID, HVNTID) always use intra-subject contrasts ("isc"), as the guidances specify, regardless of design. Pass model_type to override ("lmer", "anova", "isc", or "nlme"). Within-subject variability uses the regulatory ANOVA estimator for the "lmer"/"anova"/"isc" paths and the treatment-specific mixed-model estimator for "nlme".

Examples

# A replicate-design crossover in long (PPTESTCD/PPORRES) format
set.seed(1)
nsub <- 24
seqs <- rep(c("TRTR", "RTRT"), length.out = nsub)
b <- stats::rnorm(nsub, sd = 0.4)
d <- do.call(rbind, lapply(seq_len(nsub), function(i) {
  trt <- strsplit(seqs[i], "")[[1]]
  data.frame(
    subject = i, sequence = seqs[i], period = seq_along(trt), treatment = trt,
    PPTESTCD = "auclast",
    PPORRES = exp(log(100) + ifelse(trt == "T", 0.04, 0) + b[i] +
                    stats::rnorm(length(trt), sd = 0.45)),
    stringsAsFactors = FALSE
  )
}))
be_assess(d, reference_col = "treatment", reference_value = "R",
          endpoints = "auclast", regulator = "EMA")
#> Warning: Units were not found in the data; omitting the `units` column. Supply units via a PKNCAresults object or `PPSTRESU`/`PPORRESU` columns.
#> Bioequivalence assessment: EMA (model_type lmer, 90% CI)
#> Design: full_replicate
#> 
#>  endpoint test  n         design gm_reference gm_reference_lower
#>   auclast    T 24 full_replicate       114.44              95.46
#>  gm_reference_upper gm_test gm_test_lower gm_test_upper gmr_percent ci_lower
#>               137.2  112.15         93.55        134.46          98    85.72
#>  ci_upper cvwr_percent cvwt_percent  swr limit_lower limit_upper criterion
#>    112.04        34.11        47.67 0.33       77.71      128.68        NA
#>  regulator model_type pass
#>        EMA       lmer TRUE
#> 
#> Caption: EMA bioequivalence assessment (90% CI). Geometric means and the geometric mean ratio are least-squares means from a mixed-effects model (lmerTest::lmer, Satterthwaite degrees of freedom); the 90% CI comes from exponentiated least-squares-mean differences (emmeans). Acceptance limits are widened by the within-reference variability (ABEL) and capped per the regulator. Within-formulation variances (swR, swT) are estimated by ANOVA on each formulation's replicates.
#>