dot-power_count_serial: Estimate power for count-rate equivalence

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

Estimate power for count-rate equivalence

Description

Estimate power for count-rate equivalence

Usage

.power_count_serial(
  n_per_arm,
  rate_test,
  rate_reference,
  exposure = 1,
  margin_lower = 0.8,
  margin_upper = 1.25,
  model = c("poisson", "negative-binomial"),
  dispersion = 0.1,
  alpha = 0.05,
  nsim = 5000,
  seed = NULL,
  design = c("parallel", "2x2"),
  k = NULL,
  endpoint_corr = NULL,
  type_y = NULL,
  adjust = c("none", "bonferroni", "sidak", "t", "pc", "partial-conjunction",
    "partial_conjunction", "sequential"),
  sigmaB = 0,
  Eper = c(0, 0),
  Eco = c(0, 0),
  dropout = c(0, 0),
  type_y_active = FALSE
)

Arguments

n_per_arm	Subjects per arm.
rate_test	Event rate in the test arm.
rate_reference	Event rate in the reference arm.
exposure	Exposure per subject; a scalar or one value per endpoint.
margin_lower	Lower rate-ratio margin; a scalar or one value per endpoint.
margin_upper	Upper rate-ratio margin; a scalar or one value per endpoint.
model	Count model: "poisson" or "negative-binomial".
dispersion	Positive negative-binomial dispersion parameter. The per-subject negative-binomial size is 1 / dispersion; parallel-arm totals use size n / dispersion.
alpha	One-sided significance level.
nsim	Number of simulations.
seed	Optional random seed.
design	Trial design: "parallel" or "2x2". For "2x2", n_per_arm is interpreted as subjects per sequence.
k	Number of endpoints that must demonstrate equivalence. Defaults to all supplied endpoints.
endpoint_corr	Endpoint correlation matrix used by the Gaussian copula for multi-endpoint count simulations. The default is independence.
type_y	Numeric endpoint hierarchy used with adjust = "seq": 1 for primary/co-primary endpoints and 2 for secondary endpoints. Named vectors are recommended when endpoint names are available.
adjust	Multiplicity adjustment for endpoint-wise one-sided alpha: "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 applies the same primary-gate/secondary-family rule as the continuous kernels. When k equals the number of supplied endpoints, endpoint-wise adjustment is not necessary for the all-endpoints-required intersection-union decision; the requested method is retained but a warning is issued.
sigmaB	Between-subject standard deviation on the log-rate scale for the count ⁠2x2⁠ design.
Eper	Numeric vector of length 2 containing period effects on the log-rate scale.
Eco	Numeric vector of length 2 containing carry-over effects on the log-rate scale, ordered as reference carry-over and treatment carry-over.
dropout	Numeric vector of length 2 containing dropout proportions for the two crossover sequences.
type_y_active	Internal flag indicating whether type_y is active.

Details

For design = "2x2", complete participants contribute one count under each treatment. The kernel analyzes within-participant log-rate contrasts, averages the two sequence-specific estimates to remove period effects, and applies the carry-over correction implied by Eco = c(reference_carryover, treatment_carryover). exposure is used as the log-rate offset. sigmaB is the standard deviation of a subject random intercept used in the count-generating model; it cancels from the within-participant treatment contrast. The standard error is estimated from the empirical variance of the subject-level contrasts. Participants who drop out before completing both periods do not contribute to this paired analysis.

Value

An object of class countpower containing estimated power and its binomial confidence interval.

Examples

SimTOST:::power_count(40, 0.20, 0.20, nsim = 100, seed = 1)

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