sampleSize_count_joint: Estimate sample size for joint correlated count equivalence

In SimTOST: Sample Size Estimation for Bio-Equivalence Trials Through Simulation

Estimate sample size for joint correlated count equivalence

Description

Estimate sample size for joint correlated count equivalence

Usage

sampleSize_count_joint(
  power = 0.8,
  rates,
  comparisons,
  exposure = 1,
  margin_lower = 0.8,
  margin_upper = 1.25,
  model = c("poisson", "negative-binomial"),
  dispersion = 0.1,
  alpha = 0.05,
  endpoint_corr = NULL,
  k = NULL,
  type_y = NULL,
  adjust = c("none", "bonferroni", "sidak", "t", "pc", "partial-conjunction",
    "partial_conjunction", "sequential"),
  nsim = 5000,
  seed = NULL,
  lower = 2,
  upper = 500,
  design = c("parallel"),
  list_margin_lower = NULL,
  list_margin_upper = NULL,
  optimization_method = c("fast", "step-by-step"),
  step.power = 6,
  step.up = TRUE,
  pos.side = FALSE,
  maxiter = 1000,
  ncores = 1,
  .warn_redundant_bon = TRUE
)

Arguments

power	Target joint power.
rates	Named list of equal-length endpoint-rate vectors, one per arm.
comparisons	Named list of treatment-reference arm pairs.
exposure	Exposure per subject, scalar or one value per endpoint, or a named list with one scalar/vector per arm.
margin_lower	Lower rate-ratio equivalence margin.
margin_upper	Upper rate-ratio equivalence margin.
model	Count model: "poisson" or "negative-binomial".
dispersion	Positive negative-binomial dispersion parameter.
alpha	One-sided significance level.
endpoint_corr	Endpoint correlation matrix; the default is independence.
k	Number of endpoints that must pass within every comparison.
type_y	Numeric endpoint hierarchy used with adjust = "seq": 1 for primary/co-primary endpoints and 2 for secondary endpoints.
adjust	Multiplicity adjustment within each comparison's endpoint family.
nsim	Number of simulated trials.
seed	Optional random seed.
lower	Minimum subjects per arm.
upper	Maximum subjects per arm.
design	Joint multi-arm design; currently only "parallel" is supported.
list_margin_lower	Optional named list of lower margins, one vector per comparison.
list_margin_upper	Optional named list of upper margins, one vector per comparison.
optimization_method	Search method: "fast" uses bracketing and integer bisection; "step-by-step" evaluates every candidate.
step.power	Initial power-of-two jump for the fast search.
step.up	Direction of the initial fast-search bracketing.
pos.side	Retained for compatibility with sampleSize(); count searches always return the smallest candidate reaching the target.
maxiter	Maximum number of power evaluations.
ncores	Number of worker processes used for count simulations. Set to 1 for serial execution. Parallel execution splits nsim into reproducible independent chunks and combines the resulting successes.
.warn_redundant_bon	Logical. If TRUE, warn about redundant or uncalibrated multiplicity configurations.

Value

An object of class countss containing the selected sample size, achieved joint power, confidence interval, input parameters, and the search history in table.iter and table.test. For count outcomes, table.iter has one row per evaluated candidate sample size and table.test contains complete-trial, comparator, and endpoint decision indicators for each simulated trial and candidate. The count kernel returns aggregate decision counts rather than raw endpoint-level test statistics, so component columns preserve the simulated marginal success counts.

SimTOST documentation built on Oct. 9, 2026, 5:07 p.m.