pystatsbio 1.6.0

Biotech and pharmaceutical statistical computing for Python

PyStatsBio

Biotech and pharmaceutical statistical computing for Python.

Built on PyStatistics for the general statistical computing layer. PyStatsBio provides domain-specific methods for the drug development pipeline: dose-response modeling, sample size/power, diagnostic accuracy, non-compartmental pharmacokinetics, epidemiological measures, meta-analysis, and generalized estimating equations for clustered data.

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Design Philosophy

PyStatsBio follows the same principles as PyStatistics:

1. Fail fast, fail loud — no silent fallbacks or "helpful" defaults
2. Explicit over implicit — require parameters, don't assume intent
3. R-level validation — every function is validated against a named R reference

Each function states exactly which R function it replicates and to what tolerance.

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Quick Start

# --- Clinical trial power / sample size ---
from pystatsbio import power

# Solve for sample size (two-sample t-test)
result = power.power_t_test(d=0.5, power=0.80, alpha=0.05, type="two.sample")
print(result.n)          # per-group sample size
print(result.summary())

# Solve for power given n
result = power.power_t_test(n=64, d=0.5, alpha=0.05, type="two.sample")
print(result.power)

# Paired t-test
result = power.power_paired_t_test(d=0.3, power=0.80, alpha=0.05)
print(result.n)

# Proportions
result = power.power_prop_test(p1=0.30, p2=0.50, power=0.80, alpha=0.05)
print(result.n)

# Survival (log-rank)
result = power.power_logrank(
    p1=0.60, p2=0.40, accrual=12, follow=24, power=0.80, alpha=0.05
)
print(result.n_events, result.n_total)

# Non-inferiority for means
result = power.power_noninf_mean(
    delta=0.0, sigma=1.0, margin=0.5, power=0.80, alpha=0.05
)
print(result.n)

# Crossover bioequivalence (PowerTOST method)
result = power.power_crossover_be(cv=0.20, power=0.80, alpha=0.05)
print(result.n)          # subjects per sequence

# Cluster-randomized trial
result = power.power_cluster(
    d=0.5, icc=0.05, m=20, power=0.80, alpha=0.05
)
print(result.n_clusters)


# --- Dose-response modeling ---
import numpy as np
from pystatsbio import doseresponse

dose = np.array([0.001, 0.01, 0.1, 1.0, 10.0, 100.0])
response = np.array([2.1, 3.5, 12.0, 48.0, 87.5, 97.8])

# Fit 4PL (LL.4) model
result = doseresponse.fit_drm(dose, response, model="LL.4")
print(result.params)     # CurveParams(b, c, d, e) — Hill slope, lower, upper, EC50
print(result.ec50)
print(result.summary())

# Fit 5PL (asymmetric)
result = doseresponse.fit_drm(dose, response, model="LL.5")
print(result.params)

# Extract EC50 with confidence interval
ec50_result = doseresponse.ec50(result)
print(ec50_result.ec50, ec50_result.ci_lower, ec50_result.ci_upper)

# Relative potency (reference vs. test compound)
ref_result = doseresponse.fit_drm(dose, response, model="LL.4")
test_result = doseresponse.fit_drm(dose * 3, response, model="LL.4")
rp = doseresponse.relative_potency(ref_result, test_result)
print(rp.ratio, rp.ci_lower, rp.ci_upper, rp.parallel)

# Benchmark dose (BMD/BMDL)
bmd_result = doseresponse.bmd(result, bmr=0.10)
print(bmd_result.bmd, bmd_result.bmdl)

# Batch fitting (GPU-accelerated for HTS)
# responses: (n_curves, n_doses) array
responses = np.random.rand(500, 6) * 100
batch = doseresponse.fit_drm_batch(dose, responses, model="LL.4", backend="auto")
print(batch.ec50)        # shape (500,)
print(batch.params)      # CurveParams with shape-(500,) arrays


# --- Diagnostic accuracy ---
from pystatsbio import diagnostic

scores = np.array([0.1, 0.4, 0.35, 0.8, 0.9, 0.15, 0.6, 0.75, 0.55, 0.95])
labels = np.array([0, 0, 0, 1, 1, 0, 1, 1, 0, 1])

# ROC curve + AUC
roc_result = diagnostic.roc(scores, labels)
print(roc_result.auc, roc_result.ci_lower, roc_result.ci_upper)
print(roc_result.sensitivity, roc_result.specificity)

# Compare two biomarkers (DeLong test)
scores2 = np.random.rand(10)
test_result = diagnostic.roc_test(scores, scores2, labels)
print(test_result.p_value, test_result.summary())

# Full diagnostic accuracy at a threshold
da = diagnostic.diagnostic_accuracy(scores, labels, threshold=0.5)
print(da.sensitivity, da.specificity, da.ppv, da.npv, da.lr_pos, da.lr_neg)

# Optimal cutoff selection
cutoff = diagnostic.optimal_cutoff(roc_result, method="youden")
print(cutoff.threshold, cutoff.sensitivity, cutoff.specificity, cutoff.youden_index)

# Batch AUC for biomarker panel screening
# panel: (n_biomarkers, n_subjects) array
panel = np.random.rand(200, 100)
batch_auc = diagnostic.batch_auc(panel, labels[:100] if len(labels) >= 100 else np.random.randint(0, 2, 100))
print(batch_auc.auc)     # shape (200,) — one AUC per biomarker


# --- Pharmacokinetics (NCA) ---
from pystatsbio import pk

time = np.array([0, 0.5, 1, 2, 4, 6, 8, 12, 24])
conc = np.array([0, 8.5, 12.1, 10.4, 7.2, 4.9, 3.1, 1.4, 0.3])

result = pk.nca(time, conc, route="ev", dose=100.0)
print(result.cmax, result.tmax)
print(result.auc_last, result.auc_inf)
print(result.half_life)
print(result.cl, result.vd)
print(result.aumc_last, result.mrt)
print(result.summary())


# --- Epidemiological measures ---
from pystatsbio import epi

# 2x2 table: exposed/unexposed x case/control
table = np.array([[30, 70], [10, 90]])
result = epi.epi_2x2(table)
print(result.risk_ratio, result.odds_ratio, result.summary())

# Mantel-Haenszel stratified analysis
tables = np.array([[[30, 70], [10, 90]], [[20, 80], [15, 85]]])
result = epi.mantel_haenszel(tables)
print(result.common_or, result.p_value)


# --- Meta-analysis ---
from pystatsbio import meta

effects = np.array([0.5, 0.3, 0.8, 0.6, 0.4])
se = np.array([0.1, 0.15, 0.2, 0.12, 0.18])

# Random-effects (DerSimonian-Laird)
result = meta.meta_random(effects, se)
print(result.pooled_effect, result.ci_lower, result.ci_upper)
print(result.tau2, result.I2)
print(result.summary())

# Publication bias (Egger's test)
bias = meta.funnel_test(effects, se)
print(bias.p_value)


# --- GEE (clustered data) ---
from pystatsbio import gee

y = np.random.randn(200)
X = np.random.randn(200, 3)
cluster_id = np.repeat(np.arange(40), 5)
result = gee.gee(y, X, cluster_id, family='gaussian', corstr='exchangeable')
print(result.coefficients, result.robust_se)
print(result.summary())

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Modules

Module	Status	Description
power/	Complete	Sample size and power for clinical trial designs
doseresponse/	Complete	4PL/5PL curve fitting, EC50, relative potency, BMD, batch HTS
diagnostic/	Complete	ROC, AUC, sensitivity/specificity, optimal cutoff, batch biomarker
pk/	Complete	Non-compartmental PK analysis (NCA): AUC, Cmax, CL, Vd, MRT
epi/	Complete	Epidemiological measures: 2x2 tables, rate standardization, Mantel-Haenszel
meta/	Complete	Meta-analysis: fixed/random effects, DerSimonian-Laird, REML
gee/	Complete	Generalized estimating equations for clustered/longitudinal data

power — Sample Size and Power

Function	R equivalent
power_t_test()	pwr::pwr.t.test()
power_paired_t_test()	pwr::pwr.t.test(type="paired")
power_prop_test()	pwr::pwr.2p.test()
power_fisher_test()	TrialSize::TwoSampleProportion.Equality()
power_logrank()	gsDesign::nSurv()
power_anova_oneway()	pwr::pwr.anova.test()
power_anova_factorial()	TrialSize::FactorialDesign()
power_noninf_mean()	TrialSize::TwoSampleMean.NIS()
power_noninf_prop()	TrialSize::TwoSampleProportion.NIS()
power_equiv_mean()	TrialSize::TwoSampleMean.Equivalence()
power_superiority_mean()	TrialSize::TwoSampleMean.Superiority()
power_crossover_be()	PowerTOST::sampleSize()
power_cluster()	samplesize::n.twogroup() with ICC

doseresponse — Dose-Response Modeling

Function	R equivalent
fit_drm()	drc::drm()
fit_drm_batch()	vectorized drc::drm()
ec50()	drc::ED()
relative_potency()	drc::EDcomp() with parallelism test
bmd()	drc::bmd() / BMDS

Models: LL.4 (4PL), LL.5 (5PL), W1.4 (Weibull-1), W2.4 (Weibull-2), BC.4 (Brain-Cousens hormesis).

diagnostic — Diagnostic Accuracy

Function	R equivalent
roc()	pROC::roc() with DeLong CI
roc_test()	pROC::roc.test() (DeLong)
diagnostic_accuracy()	epiR::epi.tests()
optimal_cutoff()	OptimalCutpoints::optimal.cutpoints()
batch_auc()	vectorized pROC::auc()

pk — Non-Compartmental Analysis

Function	R equivalent
nca()	PKNCA::pk.nca() / NonCompart::sNCA()

AUC methods: linear, log, linear-up/log-down (FDA default). Routes: iv (intravenous), ev (extravascular).

epi — Epidemiological Measures

Function	R equivalent
epi_2x2()	epiR::epi.2by2()
rate_standardize()	epitools::ageadjust.direct() / ageadjust.indirect()
mantel_haenszel()	stats::mantelhaen.test()

meta — Meta-Analysis

Function	R equivalent
meta_fixed()	meta::metagen(method="FE")
meta_random()	meta::metagen(method="DL") / metafor::rma()
meta_reml()	metafor::rma(method="REML")
forest_data()	meta::forest() (data extraction for plotting)
funnel_test()	metafor::regtest() / meta::metabias()

gee — Generalized Estimating Equations

Function	R equivalent
gee()	geepack::geeglm()

Families: gaussian, binomial, poisson. Correlation structures: independence, exchangeable, ar1, unstructured.

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Installation

pip install pystatsbio

# With GPU support (requires PyTorch)
pip install pystatsbio[gpu]

# Development
pip install pystatsbio[dev]

Requires Python 3.11+. Core dependencies: pystatistics, numpy, scipy.

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License

MIT

Author

Hai-Shuo ([e-mail])