# simulation: Simulate Rejection Probability and Sample Size for Student's... In blindrecalc: Blinded Sample Size Recalculation

 simulation R Documentation

## Simulate Rejection Probability and Sample Size for Student's t-Test

### Description

This function simulates the probability that a test defined by `setupStudent` rejects the null hypothesis. Note that here the nuisance parameter `nuisance` is the variance of the outcome variable sigma^2.

### Usage

``````simulation(
design,
n1,
nuisance,
recalculation = TRUE,
delta_true,
iters = 1000,
seed = NULL,
allocation = c("approximate", "exact"),
...
)
``````

### Arguments

 `design` Object of class `Student` created by `setupStudent`. `n1` Either the sample size of the first stage (if `recalculation = TRUE` or the toal sample size (if `recalculation = FALSE`). `nuisance` Value of the nuisance parameter. For the Student's t-test this is the variance. `recalculation` Should the sample size be recalculated after n1 n1 patients are recruited? `delta_true` effect measure under which the rejection probabilities are computed `iters` Number of simulation iterations. `seed` Random seed for simulation. `allocation` Whether the allocation ratio should be preserved exactly (`exact`) or approximately (`approximate`). `...` Further optional arguments.

### Details

The implementation follows the algorithm in Lu (2019): Distribution of the two-sample t-test statistic following blinded sample size re-estimation. Pharmaceutical Statistics 15: 208-215. Since Lu (2019) assumes negative non-inferiority margins, the non-inferiority margin of `design` is multiplied with -1 internally.

### Value

Simulated rejection probabilities and sample sizes for each nuisance parameter.

### Examples

``````d <- setupStudent(alpha = .025, beta = .2, r = 1, delta = 3.5, delta_NI = 0,
alternative = "greater", n_max = 156)
simulation(d, n1 = 20, nuisance = 5.5, recalculation = TRUE, delta_true = 3.5)

``````

blindrecalc documentation built on Oct. 4, 2023, 5:06 p.m.