mrt_binary_ss {MRTSampleSizeBinary}

Calculate sample size for binary outcome MRT

Description

Returns sample size needed to achieve a specified power for the hypothesis test of marginal excursion effect (see Details) in the context of an MRT with binary outcomes with small sample correction using F-distribution. See the vignette for more details.

Usage

mrt_binary_ss(
  avail_pattern,
  f_t,
  g_t,
  beta,
  alpha,
  p_t,
  gamma,
  b,
  exact = FALSE,
  less_than_10_possible = FALSE
)

Arguments

avail_pattern

A vector of length m that is the average availability at each time point

f_t

Defines marginal excursion effect MEE(t) under alternative together with beta. Assumed to be matrix of size m*p.

g_t

Defines success probability null curve together with alpha. Assumed to be matrix of size m*q.

beta

Length p vector that defines marginal excursion effect MEE(t) under alternative together with f_t.

alpha

Length q vector that defines success probability null curve together with g_t.

p_t

Length m vector of Randomization probabilities at each time point.

gamma

Desired Type I error

b

Desired Type II error

exact

Determines if exact n or ceiling will be returned

less_than_10_possible

If TRUE, returns sample size (instead of error) even if the calculated sample size is <= 10. Setting to TRUE is not recommended. Defaults to FALSE.

Details

When the calculator finds out that a sample size less than or equal to 10 is sufficient to attain the desired power, the calculator does not output the exact sample size but produces an error message. This is because the sample size calculator is based on an asymptotic result, and in this situation the sample size result may not be as accurate. (A small sample correction is built in the calculator, but even with the correction the sample size result may still be inaccurate when it is <= 10.) In general, when the output sample size is small, one might reconsider the following: (1) whether you are correctly or conservatively guessing the average of expected availability, (2) whether the duration of study is too long, (3) whether the treatment effect is overestimated, and (4) whether the power is set too low.

Value

Sample size to achieve desired power.

Examples

mrt_binary_ss(tau_t_1, f_t_1, g_t_1, 
                                         beta_1, alpha_1, p_t_1, 
                                         0.05, .2, FALSE)