dtl_app_get_alpha_t_sim {dtlcor} | R Documentation |
Numerical minimum significance level for the final stage under drop-the-losers (DTL) design
Description
Get numerical minimum significance level alpha_t (minimum of alpha_s) for the final analysis considering the ranges of response rate q and hazard ratio of responders and non-responders gamma given a pre-specified FWER alpha
Usage
dtl_app_get_alpha_t_sim(
nsim = 1e+05,
n,
N,
q_seq,
gamma_seq,
alpha,
fix_rho = NULL,
sel_g_func = sel_g_func_default,
...
)
Arguments
nsim |
Number of replicates. |
n |
Number of patients per treatment arm at the DTL look |
N |
Total number of patients in both selected and control arms at final analysis. |
q_seq |
A vector of response rates under the null (can be 95% CI). |
gamma_seq |
A vector of hazards ratios of responders and non-responders (can be 95% CI). |
alpha |
A pre-specified FWER. |
fix_rho |
Use fixed correlation coefficient or use theoretical upper bound to get alpha_t. If = NULL, then it uses upper bound; else if = real number between 0 and 1, then it use such number as fixed correlation coefficient. |
sel_g_func |
Arm-select function. The default function is sel_g_func_default(W_2, W_1, delta). Users can define their own arm-select function. The format of the function must be function_name(W_2, W_1, ...). The return values must be 1 (arm 1 is selected) or 2 (arm 2 is selected) or 0 (stop for futility). |
... |
Other arguments from sel_g_func. |
Value
A list of two data frames for numerical minimum significance level alpha_t and significance level alpht_s given all combinations of q_seq and gamma_seq.
Examples
# Inputs
set.seed(1000)
nsim = 100000
n = 80
N = 152
q_seq = seq(0.19, 0.32, 0.01)
gamma_seq = seq(0.14, 0.34, 0.01)
alpha = 0.025
delta = 0.05
# Use fixed correlation coefficient
dtl_app_get_alpha_t_sim(nsim, n, N, q_seq, gamma_seq, alpha,
fix_rho = 1, delta = delta)
# Use theoretical upper bound
dtl_app_get_alpha_t_sim(nsim, n, N, q_seq, gamma_seq, alpha,
fix_rho = NULL, delta = delta)