Compute the power using LRT Re-randomization test
PRIMEplus.Power(nmax=500, rand_ratio=0.5, effect_p=0.6,
enroll_rate=380*0.25/6, lambda1=0.117, HR=0.5, tau=12*5, t1=1,
maxiter=100000, stopTol=1e-4, alpha=0.05, num_rand=1000, nsim=10000,
print=0, min.sample.size=50, min.n.event=5, min.per.trt=0.25)A list containing the power and the number of simulated datasets used in the calculation.
Sample size
Probability of assignment to treatment arm
Vector for proportion of responders in the treatment arm at baseline (see details)
Enrollment rate in subjects per month
Baseline hazard in terms of months
Vector of hazard ratios for responders against controls (see details)
Total study duration
Delayed duration in months
Maximum number of iterations in the EM algorithm. The default is 100000.
Stopping tolerance in the EM algorithm. The default is 1e-4.
Significance level. The default is 0.05.
The number of replications in the re-randomization test. The default is 1000.
The number of simulations. The default is 1000.
0 or 1 to print information. The default is 0.
Minimum sample size. The default is 50.
Minimum number of events. The default is 5.
Minimum proportion of controls and treated subjects. The default is 0.25.
Zhenzhen Xu <Zhenzhen.Xu@fda.hhs.gov>, Yongsoek Park <yongpark@pitt.edu> and Bin Zhu <bin.zhu@nih.gov>
The length and order of effect_p must be the same as HR. Both of these vectors
should contain information only for the groups of responders. For example, if there are
full responders and partial responders, then effect_p and HR would be vectors
of length two.
For each simulation, a simulated data set is created from the
generate_data function and then an estimated p-value is computed
by calling PRIMEplus.ReRand.LRT.
The power is calculated as the proportion of iterations whose estimated p-value
was less than or equal to alpha.
PRIMEplus.ReRand.LRT