openpkflow.be

Bioequivalence convenience analysis: paired 2x2 TOST, GMR + 90% CI, intra-subject CV. For formal ANOVA-based BE, NTI, ABEL/RSABE, replicate designs, and validation fixtures, export to BioEqPy.

Public API

Symbol	Type	Description
BEStudy	class	Entry point: __init__(df, parameter, ...), .analyze() -> BEResult, .from_nca_results()
BEResult	dataclass	Analysis result: gmr, gmr_lower_90ci, gmr_upper_90ci, bioequivalent, cv_intra_pct, subjects_df, .summary(), .report()
BETOSTResult	dataclass	Low-level TOST output from be_tost()
be_tost(reference, test, ...)	function	Core TOST computation
BEStudy.to_bioeqpy_dataframe()	method	Export BioEqPy-ready long-format BE input
BEStudy.to_bioeqpy_csv(path)	method	Write BioEqPy-ready CSV input

BEStudy

BEStudy(
    df,                      # wide-format DataFrame
    parameter="AUCinf",      # label for output
    *,
    reference_col="reference",
    test_col="test",
    subject_col="subject",
    sequence_col="sequence", # or None; silently dropped if default name absent
)

Required DataFrame columns: subject_col, reference_col, test_col.

sequence_col: - Default "sequence" — silently dropped if absent from the DataFrame. - Any other name — raises ValueError if absent. - Pass None explicitly to indicate no sequence column.

.analyze()

result = study.analyze(
    be_lower=0.80,   # FDA/EMA standard lower limit
    be_upper=1.25,   # FDA/EMA standard upper limit
    alpha=0.05,      # one-sided significance level (90% CI)
)

Returns a BEResult.

.from_nca_results()

BEStudy.from_nca_results(
    reference_results,   # NCASummaryResults
    test_results,        # NCASummaryResults
    parameter="AUCinf",  # "AUCinf", "AUClast", or "Cmax"
)

Matches subjects by ID. Raises ValueError if no common subjects are found.

.to_bioeqpy_dataframe()

from bioeqpy import analyze

table = study.to_bioeqpy_dataframe()
results = analyze(table, parameters=["AUCinf"])

Exports long-format columns expected by BioEqPy: subject, sequence, period, treatment, and the selected PK parameter. The current export supports standard TR/RT 2x2 crossover studies and requires a sequence column.

.to_bioeqpy_csv(path)

Writes the same BioEqPy-ready long-format table to CSV.

BEResult

Attribute	Type	Description
parameter	str	PK parameter label
n	int	Number of subjects
gmr	float	Geometric Mean Ratio (test/reference)
gmr_lower_90ci	float	Lower bound of 90% CI
gmr_upper_90ci	float	Upper bound of 90% CI
be_lower	float	Acceptance limit (lower)
be_upper	float	Acceptance limit (upper)
bioequivalent	bool	True if 90% CI within limits
cv_intra_pct	float	Intra-subject CV%
subjects_df	DataFrame	Per-subject table (subject, reference, test, ratio, log_diff)

.summary()

Returns an ASCII table with GMR, 90% CI, acceptance limits, CV%, and verdict.

.report(path, format=None)

Writes an HTML or Markdown report. Format inferred from file extension; override with format="html" or format="markdown".

be_tost()

from openpkflow.be.methods import be_tost

result = be_tost(
    reference,          # list of reference values (positive floats)
    test,               # list of test values (positive floats, same length)
    *,
    be_lower=0.80,
    be_upper=1.25,
    alpha=0.05,
)

Raises ValueError for mismatched lengths, n < 2, non-positive values, or invalid acceptance limits.

Acceptance limits reference

Product type	be_lower	be_upper
Standard (FDA/EMA)	0.80	1.25
NTI (FDA)	0.90	1.1111

Statistical method

Log-transformed TOST (Schuirmann 1987). Within-subject log-differences are used for GMR estimation and CI construction. Intra-subject CV is derived as sqrt(exp(s_d^2) - 1) * 100 (Chow & Liu 2008, eq. 3.3.4).

Reference: Schuirmann DJ (1987). J Pharmacokinet Biopharm 15(6):657-680. FDA guidance: Statistical Approaches to Establishing Bioequivalence (2001).