| SEU_power_comparison_Power_vs_n {RARfreq} | R Documentation |
Comparison of Powers for Sample Sizes under Different SEU Randomization Methods (Binary Responses)
Description
Compares the power of tests under different sample sizes for the same treatment effects and design through matrices and plots.
Usage
SEU_power_comparison_Power_vs_n(n_seq, nstart_seq, p, urn_comp,
nstop_mat, replication, group_allo, add_rule_index, add_rule, add_rule_full,
sig_level)
Arguments
n_seq |
A sequence of settings' number of patients. The default is c(50, 100, 150, 200). |
nstart_seq |
The burn-in sample size of each arm. The default is n_seq/20 = c(2, 5, 8, 10). |
p |
A vector of probabilities containing probabilities for each treatment arm (where the first element refers to the control arm). The length of p should correspond to the number of treatment arms. The default is p = c(0.3, 0.3, 0.6). |
urn_comp |
A vector of current urn composition. The default is NULL, which indicates no ball in the urn. |
nstop_mat |
A matrix of sample size stopping caps for each arm. Each row corresponds to each n in n_seq, and each column represents each arm. The trial stops if at least one arm reaches the corresponding cap. The default is NULL, which means no cap. |
replication |
the number of replications of the simulation. The default is 100. |
group_allo |
A number or a vector of group size(s) for allocation. If a number is given, the allocation ratios will be updated for each batch of group_allo samples. If a vector is given, the allocation ratios will be updated sequentially in group according to the vector. The group_allo will be applied to all n (from each n_seq). Any value greater than n will be omitted. The default is group_allo=1, which is the same as group_allo = seq(nstart*length(p)+1,n). |
add_rule_index |
Supply a number of 1, 2 or 3 indicting the addition rules to target allocation functions. 1 = randomized play-the-winner (RPW) rule that targets the urn allocation 2 = the SEU model that targets Neyman allocation; 3 = the SEU model that targets Rosenberger allocation;' 4 = the SEU model that assigns probability of 0.6+1/K to winner at each step. The default is 1. |
add_rule |
Supply a user-specified addition rules function of x.df and arms when add_rule_index is NULL. Default is NULL. (See SEU_BINARY_raw for details on x.df and arms.) |
add_rule_full |
Indicator of reference data for updating addition rule. If TRUE, the addition rule is updated by full observation at each group allocation. If FALSE,the addition rule is updated by each group observation. The default is FALSE for add_rule_index=1 and TRUE otherwise. |
sig_level |
Significant level (one-sided). The default is 0.05. |
Details
'SEU_power_comparison_Power_vs_n' reads different sample sizes as well as the corresponding burn-in size and outputs allocation, estimated rates and powers.
Value
Allocation - Average and standard deviation (SD) of allocation distribution
Estimation - Average and standard deviation of treatment effect
Power_chisq - Average power of Chi-square test
Power_oneside - Average power of one-sided Welch T-test performed for each of the k-th arm against H0: p_1>p_k without multiplicity adjustment
Plot - Four figures of results: 1) Allocation mean and SD, 2) Estimated mean response and SD, 3) Power of Chi-square test, 4) Power of one-sided proportion test
Examples
## Default setting
SEU_power_comparison_Power_vs_n(
n_seq = seq(from = 50, to = 200, by = 50),
nstart_seq = round(seq(from = 50, to = 200, by = 50) / 20),
p = c(0.3, 0.3, 0.6),
urn_comp = NULL,
nstop_mat = NULL,
replication = 4,
group_allo = 1,
add_rule_index = 1,
add_rule = NULL,
add_rule_full = FALSE,
sig_level = 0.05
)