PRIMEplus.SampleSize {PRIMEplus}R Documentation

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)

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.

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.

NOTE:
It is important to note that the power for a given sample size is estimated by running a simulation. Thus, by setting a different seed, a different result may be returned. Therefore, to ensure a more precise estimated sample size, set the option nsim to a large value and/or run this function several times by setting different seeds and examine the distribution of returned sample sizes.

Value

A list containing the sample size and power.

Author(s)

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

See Also

PRIMEplus.Power


[Package PRIMEplus version 1.0.16 Index]