test_par_rom: Simulate a Parallel Design and Test Ratio of Means (ROM)

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

Simulate a Parallel Design and Test Ratio of Means (ROM)

Description

Simulates a parallel-group design and performs equivalence testing using the ratio of means (ROM) approach. This function evaluates whether the treatment and reference groups are equivalent based on predefined equivalence margins and hypothesis testing criteria.

Usage

test_par_rom(
  n,
  muT,
  muR,
  SigmaT,
  SigmaR,
  lequi_tol,
  uequi_tol,
  alpha,
  dropout,
  typey,
  adseq,
  k,
  arm_seedT,
  arm_seedR,
  TART,
  TARR,
  vareq
)

Arguments

n	integer number of subjects per arm
muT	vector mean of endpoints on treatment arm
muR	vector mean of endpoints on reference arm
SigmaT	matrix covar-variance matrix on treatment arm across endpoints
SigmaR	matrix covar-variance matrix on reference arm across endpoints
lequi_tol	vector lower equivalence tolerance band across endpoints
uequi_tol	vector upper equivalence tolerance band across endpoints
alpha	vector alpha value across endpoints
dropout	vector of size 2 with dropout proportion per arm (T,R)
typey	vector with positions of primary endpoints
adseq	boolean is used a sequential adjustment?
k	integer minimum number of equivalent endpoints
arm_seedT	integer seed for the simulation on treatment arm
arm_seedR	integer seed for the simulation on reference arm
TART	double treatment allocation rate for the treatment arm
TARR	double treatment allocation rate for the reference arm
vareq	Boolean. If TRUE, assumes equal variance between arms and applies Schuirmann's two one-sided tests (TOST) for equivalence using a pooled variance.

Details

The function simulates a parallel-group study design and evaluates equivalence using the ratio of means (ROM) approach. It accounts for dropout rates and treatment allocation proportions while generating simulated data based on the specified covariance structure. The test statistics are computed, and a final equivalence decision is made based on the predefined number of required significant endpoints (k). If sequential testing (adseq) is enabled, primary endpoints must establish equivalence before secondary endpoints are evaluated. When vareq = TRUE, the test assumes equal variances between groups and applies Schuirmann's two one-sided tests (TOST).

Value

A numeric matrix containing the simulated hypothesis test results. The first column represents the overall equivalence decision, where 1 indicates success and 0 indicates failure. The subsequent columns contain the hypothesis test results for each endpoint, followed by mean estimates for the reference and treatment groups, and standard deviations for the reference and treatment groups.

SimTOST documentation built on April 3, 2025, 9:05 p.m.