Description Usage Arguments Value References Examples
View source: R/SSize_FixAlter.R
A function obtains maximum sample sizes and associated expected values for a group sequential design under a generalized gamma survival distribution or a log-logistic survival distribution for a given dropout censoring distribution.
1 2 | SSize.FixAlter(t, R, T, FUN.C, para0, para1 = NULL, haz.r, rho = 0,
eta = 1, theta = 0, px = 0.5, spf = 1, alpha = 0.05, power = 0.8)
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t |
the interim analysis time (vector). |
R |
the recuritment duration. |
T |
the study duration. |
FUN.C |
the cumulative distribution function of dropout censoring. |
para0 |
c(q0,mu0,sigma0), parameters of an assumed generalized gamma distribution for the control arm. A character string q0="LLG" indiactes an assumed log-logistic survival distribution F_0(y;ξ,ζ)=1/(1+(y/ξ)^{-ζ}) for the control arm, where ξ = mu0 and ζ = sigma0. |
para1 |
c(q1,mu1,sigma1), parameters of an assumed generalized gamma distribution for the treatment arm. A character string q1="LLG" indiactes an assumed log-logistic survival distribution F_1(y;ξ,ζ)=1/(1+(y/ξ)^{-ζ}) for the treatment arm, where ξ = mu1 and ζ = sigma1. |
haz.r |
the hazard ratio of the treatment arm to the control arm (numeric or function). |
rho |
the power in the weight of the Harrington-Fleming statistic. ρ=0 for the logrank test; ρ=1 for the Wilcoxon test. |
eta, theta |
parameters of the entry distribution with η ≥ -θ/R and η >0 (θ=0 for the Uniform dropout censoring). |
px |
the proportion of patients assigned to the treatment arm. The default is px = 0.5 indicating 1:1 allocation. |
spf |
1 = O<e2><80><99>Brien-Fleming-type; 2 = Pocock-type alpha-spending function. The default is spf = 1. |
alpha |
the type I error. The default is alpha = 0.05. |
power |
A desired value of the power. The default is power = 0.8. |
MaxSize |
the maximum sample size. |
ExpSize |
the expected sample size. |
ExpEvent |
the expected number of events. |
A.power |
actual achieved power. |
Info.fractions |
information fractions at times of all the interim analyses. |
boundary |
the monitoring boundary values of the standardized Harrington-Fleming statistic at all the interim analyses. |
Hsu, C.-H., Chen, C.-H, Hsu, K.-N. and Lu, Y.-H. (2018). A useful design utilizing the information fraction in a group sequential clinical trial with censored survival data. To appear in Biometrics.
Azzalini, A. and Genz, A. (2015). The R package ‘mnormt’: The multivariate normal and 't' distributions (version 1.5-3). URL http://azzalini.stat.unipd.it/SW/Pkg-mnormt.
Casper, C. and Perez, O. A. (2014). The R package ‘ldbounds’: Lan-DeMets method for group sequential boundaries (version 1.1-1). URL https://cran.r-project.org/web/packages/ldbounds/index.html.
Jackson, C., Metcalfe, P. and Amdahl, J. (2017). The R package ‘flexsurv’: Flexible Parametric Survival and Multi-State Models (version 1.1). URL https://github.com/chjackson/flexsurv-dev.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | # Assume an exponential (log-logistic) survival distribution
# with q0=sigma0=1, mu0=0.367 (xi0=1, zeta0=1.75) for the control arm,
# a uniform patient entry (eta=1,theta=0) and a uniform dropout censoring distribution Unif(0,h)
# having a 15% censoring probability (lfu=0.15) for a study with R=2, T=3 and the interim
# analysis time at t=1,1.5,2,2.5.
# To obtain the required h for the uniform dropout censoring distribution.
Find.h(lfu=0.15, R=2, T=3, q=1, mu=0.367, sigma=1, eta=1, theta=0) ## exponential
Find.h(lfu=0.15, R=2, T=3, q="LLG", mu=1, sigma=1.75, eta=1, theta=0) ## log-logistic
# To obtain the maximum sample size for testing a treatment difference of a hazard ratio of 2/3
# with a type-I error of 0.05 and a power of 0.8.
SSize.FixAlter(t=c(1,1.5,2,2.5), R=2, T=3, FUN.C=function(y) punif(y,0,7.018),
para0=c(1,0.367,1), para1=NULL, haz.r=2/3, rho=0, eta=1, theta=0) # exponential
SSize.FixAlter(t=c(1,1.5,2,2.5), R=2, T=3, FUN.C=function(y) punif(y,0,7.211),
para0=c("LLG",1,1.75), para1=NULL, haz.r=2/3, rho=0, eta=1, theta=0) # log-logistic
# To obtain the maximum sample size for testing H_0:F_0=F_1 with a type-I error of 0.05
# and a power of 0.8, where F_1 is an exponential (log-logistic) distribution
# with the parameter para1=c(1,0.772,1) (para1=c("LLG",1.5,1.75)).
SSize.FixAlter(t=c(1,1.5,2,2.5), R=2, T=3, FUN.C=function(y) punif(y,0,7.018),
para0=c(1,0.367,1), para1=c(1,0.772,1), haz.r=NULL, rho=0, eta=1, theta=0) # exponential
SSize.FixAlter(t=c(1,1.5,2,2.5), R=2, T=3, FUN.C=function(y) punif(y,0,7.211),
para0=c("LLG",1,1.75), para1=c("LLG",1.5,1.75), haz.r=NULL, rho=0, eta=1, theta=0) # log-logistic
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