power_summary {MRTSampleSizeBinary}

Calculate sample size at a range of power levels.

Description

Returns sample sizes needed to achieve a range of power levels 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

power_summary(
  avail_pattern,
  f_t,
  g_t,
  beta,
  alpha,
  p_t,
  gamma,
  power_levels = seq(from = 0.6, to = 0.95, by = 0.05)
)

Arguments

avail_pattern

A vector of length T 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 T*p.

g_t

Defines success probability null curve together with alpha. Assumed to be matrix of size T*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 T vector of Randomization probabilities at each time point.

gamma

Desired Type I error

power_levels

Vector of powers to find sample size for.

Details

The sample size calculator is based on an asymptotic result with a small sample correction. 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, because in this situation the sample size result may not be as accurate. 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

Dataframe containing needed sample size to achieve user-specified power values.

Examples

           power_summary(tau_t_1, f_t_1, g_t_1,
                                   beta_1, alpha_1, p_t_1, 0.05)