Student-class {blindrecalc}R Documentation

Student's t test

Description

This class implements Student's t-test for superiority and non-inferiority tests. A trial with continuous outcomes of the two groups E and C is assumed. If alternative == "greater" the null hypothesis for the mean difference \Delta = \mu_E - \mu_C is

H_0: \Delta \leq -\delta_{NI} \textrm{ vs. } H_1: \Delta > -\delta_{NI}.

Here, \delta_{NI} \geq 0 denotes the non-inferiority margin. For superiority trials,\delta_{NI} can be set to zero (default). If alternative=="smaller", the direction of the effect is changed.

The function setupStudent creates an object of class Student that can be used for sample size recalculation.

Usage

setupStudent(
  alpha,
  beta,
  r = 1,
  delta,
  delta_NI = 0,
  alternative = c("greater", "smaller"),
  n_max = Inf,
  ...
)

Arguments

alpha

One-sided type I error rate.

beta

Type II error rate.

r

Allocation ratio between experimental and control group.

delta

Difference of effect size between alternative and null hypothesis.

delta_NI

Non-inferiority margin.

alternative

Does the alternative hypothesis contain greater (greater) or smaller (smaller) values than the null hypothesis.

n_max

Maximal overall sample size. If the recalculated sample size is greater than n_max it is set to n_max.

...

Further optional arguments.

Details

The nuisance parameter is the variance \sigma^2. Within the blinded sample size recalculation procedure, it is re-estimated by the one-sample variance estimator that is defined by

\widehat{\sigma}^2 := \frac{1}{n_1-1} \sum_{j \in \{T, C \}} \sum_{k=1}^{n_{1,j}}(x_{j,k} - \bar{x} )^2,

where x_{j,k} is the outcome of patient k in group j, n_{1,j} denotes the first-stage sample size in group j and \bar{x} equals the mean over all n_1 observations. The following methods are available for this class: toer, pow, n_dist, adjusted_alpha, and n_fix. Check the design specific documentation for details.

Value

An object of class Student.

References

Lu, K. (2019). Distribution of the two-sample t-test statistic following blinded sample size re-estimation. Pharmaceutical Statistics 15(3): 208-215.

Examples

d <- setupStudent(alpha = .025, beta = .2, r = 1, delta = 3.5, delta_NI = 0,
                  alternative = "greater", n_max = 156)

[Package blindrecalc version 1.0.1 Index]