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Log-rank test simulation. P-value and z-statistics (parallel)

log_rank_sim.Rd

For log-rank test simulation. Single-stage p-value or two-stage statistics. Return the simulated type I error, empirical mean and variance of Z-statistics of log-rank test

Usage

log_rank_sim(data_C, data_E, sim_size, n, alpha, sided)

Arguments

data_C

Survival data of control group generated by expo_gen_2stages

data_E

Survival data of experiment group generated by expo_gen_2stages

sim_size

simulation times

n

Sample size in each arm

alpha

Stated type I error level

sided

"two_sided" for two-sided test, "greater" for one-sided superiority test

Value

A list with the following components:

rejection

The proportion of simulations where the log-rank test statistic is less than or equal to alpha.

z_stats

A numeric vector of the z statistics W/sigma for each simulation.

var_w

The variance of statistics $W$ for correlation calculation.

Examples

sim_size <- 5000 
N <- 100
r <- 60
acc_time <- N / r
cen_time <- 1
lambda_H1 <- 0.9
HR <- 1.7
lambda_H0 <- 0.9 * 1.7
change_time <- 1
interim <- 0.6 * acc_time
n <- ceiling(N / 2)
alpha <- 0.05

data_C <- expo_gen_2stages(N = n * sim_size, acc_time = acc_time, 
                       lambda = lambda_H0, dist = 'exp', 
                       cen_time = cen_time,arm = 0, interim = interim)    
data_E_H0 <- expo_gen_2stages(N = n * sim_size, acc_time = acc_time, 
                       lambda = lambda_H0, dist = 'exp', 
                       cen_time = cen_time,arm = 1, interim = interim)
lr_h0_int <- log_rank_sim(data_C = data_C[ , c(2,3,1)], data_E = data_E_H0[ , c(2,3,1)], 
                        sim_size =  sim_size, n = n, alpha = alpha, sided = 'greater')                                
#> Error in makeCluster(n_cores): could not find function "makeCluster"
# print(lr_h0_int$rejection)
# print(lr_h0_int$z_stats)
# print(lr_h0_int$var_w)