Sample Size Calculator for MRT with Binary Outcomes

Documentation for package ‘MRTSampleSizeBinary’ version 0.1.1

alpha_1	Vector that defines the success probability null curve.
beta_1	Vector that defines the MEE under the alternative hypothesis.
compute_m_sigma	Computes "M" and "Sigma" matrices for the sandwich estimator of variance-covariance matrix.
compute_ncp	Computes the non-centrality parameter for an F distributed random variable in the context of a MRT with binary outcome.
f_t_1	A matrix defining the MEE under the alternative hypothesis.
g_t_1	A matrix defining the success probability null curve.
is_full_column_rank	Check if a matrix is full column rank.
max_samp	Returns default maximum sample size to end power_vs_n_plot().
min_samp	Compute minimum sample size.
mrt_binary_power	Calculate power for binary outcome MRT
mrt_binary_ss	Calculate sample size for binary outcome MRT
m_matrix_1	An example matrix for "bread" of sandwich estimator of variance.
power_summary	Calculate sample size at a range of power levels.
power_vs_n_plot	Returns a plot of power vs sample size in the context of a binary outcome MRT. See the vignette for more details.
p_t_1	A vector of randomization probabilities for each time point.
sigma_matrix_1	An example matrix for "meat" of sandwich estimator of variance.
tau_t_1	Vector that holds the average availability at each time point.