dot-power_count_joint_serial: Estimate joint power for correlated count endpoints and...

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

Estimate joint power for correlated count endpoints and multiple comparisons

Description

Each simulated trial contains all arms and endpoints. Endpoint counts are generated with their requested marginal Poisson or negative-binomial distributions and a Gaussian-copula dependence structure. Every comparison must pass at least k endpoints for the trial to count as a success.

Usage

.power_count_joint_serial(
  n_per_arm,
  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,
  design = c("parallel"),
  list_margin_lower = NULL,
  list_margin_upper = NULL,
  type_y_active = FALSE
)

Arguments

n_per_arm	Subjects in each arm. This joint implementation supports parallel-group designs.
rates	Named list of equal-length endpoint-rate vectors, one per arm.
comparisons	Named list of length-two character vectors. The first arm is the test arm and the second is the reference arm.
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, scalar or a named list with one scalar/vector per arm.
alpha	One-sided significance level, scalar or one value per endpoint.
endpoint_corr	Positive-definite latent Gaussian correlation matrix across endpoints. 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 selected endpoint family: "none", "bonferroni", "sidak", "t", or "seq"/"sequential". The "t" option uses Mielke's strong k-out-of-m calibration alpha / (m - k + 1); legacy partial-conjunction labels are accepted. Sequential testing uses the primary gate and secondary-family rule used by the continuous kernels. When all supplied endpoints are required (k equals the endpoint count), endpoint-wise adjustment is not necessary for the intersection-union decision; a requested adjustment remains available with a warning.
nsim	Number of simulated trials.
seed	Optional random seed.
design	Joint multi-arm design; currently only "parallel" is supported because multiple reference arms do not define a single standard 2x2 crossover design.
list_margin_lower	Optional named list of lower margins, one vector per comparison. Each vector is scalar or has one value per endpoint.
list_margin_upper	Optional named list of upper margins, one vector per comparison. Each vector is scalar or has one value per endpoint.
type_y_active	Internal flag indicating whether type_y is active.

Value

An object of class countpower containing joint power and a binomial confidence interval.

Examples

rates <- list(TEST = c(.20, .20), REF = c(.20, .20), ALT = c(.20, .20))
SimTOST:::power_count_joint(100, rates, list(REF = c("TEST", "REF"),
                     ALT = c("TEST", "ALT")), nsim = 100, seed = 1)

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