PRIMEplus.SampleSize: Sample Size

Description

Compute the sample size for a given power

Usage

PRIMEplus.SampleSize(power=0.8, 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, min.N=100, max.N=700,
              tol.power=0.01, tol.N=1, print=1,
              min.sample.size=50, min.n.event=5, min.per.trt=0.25)

Value

A list containing the sample size and power.

Arguments

power: The desired power. The default is 0.8.
rand_ratio: Allocation ratio
effect_p: Vector for proportion of responders in the treatment arm at baseline (see details)
enroll_rate: Enrollment rate in subjects per month
lambda1: Baseline hazard in terms of months
HR: Vector of hazard ratios for responders against controls (see details)
tau: Total study duration
t1: Delayed duration
maxiter: Maximum number of iterations in the EM algorithm. The default is 100000.
stopTol: Stopping tolerance in the EM algorithm. The default is 1e-4.
alpha: Significance level. The default is 0.05.
num_rand: Number of replications in the re-randomization test. The default is 1000.
nsim: Number of simulations in computing power (see Details). The default is 10000.
min.N: Lower bound for the sample size. The default is 100.
max.N: Upper bound for the sample size. The default is 700.
tol.power: Stopping tolerance for the power. The default is 0.01.
tol.N: Stopping tolerance for the sample size. The default is 1.
print: 0 or 1 to print information. The default is 1.
min.sample.size: Minimum sample size. The default is 50.
min.n.event: Minimum number of events. The default is 5.
min.per.trt: Minimum proportion of controls and treated subjects. The default is 0.25.

Author

Zhenzhen Xu <Zhenzhen.Xu@fda.hhs.gov>, Yongsoek Park <yongpark@pitt.edu> and Bin Zhu <bin.zhu@nih.gov>

Details

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.

This uses a bisection method to estimate the sample size. At each iteration, the estimated power power_est is computed using PRIMEplus.Power for a given sample size holding all other parameters fixed. The algorithm terminates when abs(power - power_est) <= tol.power or when the length of the estimated interval containing the sample size is less than or equal to tol.N.