, Chel Hee Lee [cre,
aut] | Title: | Generalized Two Arms Clinical Trial Sample Size Calculation |
|---|---|
| Description: | Two arms clinical trials required sample size is calculated in the comprehensive parametric context. The calculation is based on the type of endpoints(continuous/binary/time-to-event/ordinal), design (parallel/crossover), hypothesis tests (equality/noninferiority/superiority/equivalence), trial arms noncompliance rates and expected loss of follow-up. Methods are described in: Chow SC, Shao J, Wang H, Lokhnygina Y (2017) <doi:10.1201/9781315183084>, Wittes, J (2002) <doi:10.1093/epirev/24.1.39>, Sato, T (2000) <doi:10.1002/1097-0258(20001015)19:19%3C2689::aid-sim555%3E3.0.co;2-0>, Lachin J M, Foulkes, M A (1986) <doi:10.2307/2531201>, Whitehead J(1993) <doi:10.1002/sim.4780122404>, Julious SA (2023) <doi:10.1201/9780429503658>. |
| Authors: | Mohsen Soltanifar [aut]
, Chel Hee Lee [cre,
aut] |
| Maintainer: | Chel Hee Lee <[email protected]> |
| License: | GPL-3 |
| Version: | 0.0.5 |
| Built: | 2024-12-12 06:48:02 UTC |
| Source: | CRAN |
This function computes the sample size required for two arms clinical trials with continuous outcome measure. Four hypothesis tests are available under two allocation designs.
getSizeMean( design = c("parallel", "crossover"), test = c("equality", "noninferiority", "superiority", "equivalence"), alpha = 0.05, beta = 0.2, sigma, k = 1, delta = 0, TTE, rho = c(0.05, 0.07), r = 0.1 )getSizeMean( design = c("parallel", "crossover"), test = c("equality", "noninferiority", "superiority", "equivalence"), alpha = 0.05, beta = 0.2, sigma, k = 1, delta = 0, TTE, rho = c(0.05, 0.07), r = 0.1 )
design |
allocation method ( |
test |
four hypothesis tests: |
alpha |
level of significance. |
beta |
type II error. |
sigma |
pooled standard deviation of two groups. |
k |
ratio of control to treatment. |
delta |
delta margin in test hypothesis. |
TTE |
target treatment effect or effect size. |
rho |
vector of length 2, positive noncompliance rates of two arms. |
r |
projected proportion of trial uniform loss of follow-up. |
sample size per arm.
# Ex 1. (n_trt=91, n_ctl=91) getSizeMean(design="parallel", test="equality", alpha=0.05, beta=0.20, sigma=0.10, k=1, delta=0, TTE=0.05, rho=c(0.05, 0.07), r=0.1) getSizeMean(design="parallel", test="noninferiority", alpha=0.05, beta=0.20, sigma=0.10, k=1, delta=-0.05, TTE=0, rho=c(0.05, 0.07), r=0.1) # Ex 3. (n_trt=1022, n_ctl=1022) getSizeMean(design="parallel", test="superiority", alpha=0.05, beta=0.20, sigma=0.10, k=1, delta=0.05, TTE=0.07, rho=c(0.05, 0.07), r=0.1) # Ex 4. (n_trt=113, n_ctl=113) getSizeMean(design="parallel", test="equivalence", alpha=0.05, beta=0.20, sigma=0.10, k=1, delta=0.05, TTE=0.01, rho=c(0.05, 0.07), r=0.1) # Ex 5. (n_trt=23, n_ctl=23) getSizeMean(design="crossover", test="equality", alpha=0.05, beta=0.20, sigma=0.10, k=1, delta=0, TTE=0.05, rho=c(0.05, 0.07), r=0.1) # Ex 6. (n_trt=14, n_ctl=14) getSizeMean(design="crossover", test="noninferiority", alpha=0.05, beta=0.20, sigma=0.10, k=1, delta=-0.05, TTE=0, rho=c(0.05, 0.07), r=0.1) # Ex 7. (n_trt=21, n_ctl=21) getSizeMean(design="crossover", test="superiority", alpha=0.05, beta=0.20, sigma=0.10, k=1, delta=0.05, TTE=0.01, rho=c(0.05, 0.07), r=0.1) # Ex 8. (n_trt=29, n_ctl=29) getSizeMean(design="crossover", test="equivalence", alpha=0.05, beta=0.20, sigma=0.10, k=1, delta=0.05, TTE=0.01, rho=c(0.05, 0.07), r=0.1)# Ex 1. (n_trt=91, n_ctl=91) getSizeMean(design="parallel", test="equality", alpha=0.05, beta=0.20, sigma=0.10, k=1, delta=0, TTE=0.05, rho=c(0.05, 0.07), r=0.1) getSizeMean(design="parallel", test="noninferiority", alpha=0.05, beta=0.20, sigma=0.10, k=1, delta=-0.05, TTE=0, rho=c(0.05, 0.07), r=0.1) # Ex 3. (n_trt=1022, n_ctl=1022) getSizeMean(design="parallel", test="superiority", alpha=0.05, beta=0.20, sigma=0.10, k=1, delta=0.05, TTE=0.07, rho=c(0.05, 0.07), r=0.1) # Ex 4. (n_trt=113, n_ctl=113) getSizeMean(design="parallel", test="equivalence", alpha=0.05, beta=0.20, sigma=0.10, k=1, delta=0.05, TTE=0.01, rho=c(0.05, 0.07), r=0.1) # Ex 5. (n_trt=23, n_ctl=23) getSizeMean(design="crossover", test="equality", alpha=0.05, beta=0.20, sigma=0.10, k=1, delta=0, TTE=0.05, rho=c(0.05, 0.07), r=0.1) # Ex 6. (n_trt=14, n_ctl=14) getSizeMean(design="crossover", test="noninferiority", alpha=0.05, beta=0.20, sigma=0.10, k=1, delta=-0.05, TTE=0, rho=c(0.05, 0.07), r=0.1) # Ex 7. (n_trt=21, n_ctl=21) getSizeMean(design="crossover", test="superiority", alpha=0.05, beta=0.20, sigma=0.10, k=1, delta=0.05, TTE=0.01, rho=c(0.05, 0.07), r=0.1) # Ex 8. (n_trt=29, n_ctl=29) getSizeMean(design="crossover", test="equivalence", alpha=0.05, beta=0.20, sigma=0.10, k=1, delta=0.05, TTE=0.01, rho=c(0.05, 0.07), r=0.1)
This function computes the sample size required for two arms clinical trials with ordinal outcome measure. Four hypothesis tests are available under two allocation designs.
getSizeOrd( design = c("parallel", "crossover"), test = c("equality", "noninferiority", "superiority", "equivalence"), alpha = 0.05, beta = 0.2, varcatprob, k = 1, theta, delta = 0, rho = c(0.05, 0.07), r = 0.1 )getSizeOrd( design = c("parallel", "crossover"), test = c("equality", "noninferiority", "superiority", "equivalence"), alpha = 0.05, beta = 0.2, varcatprob, k = 1, theta, delta = 0, rho = c(0.05, 0.07), r = 0.1 )
design |
allocation method ( |
test |
four hypothesis tests: |
alpha |
level of significance. |
beta |
type II error. |
varcatprob |
list of two probability vectors per treatment arm |
k |
ratio of control to treatment. |
theta |
log odds ratio of outcome in treatment arm versus control arm |
delta |
delta margin in test hypothesis. |
rho |
vector of length 2, positive noncompliance rates of two arms. |
r |
projected proportion of trial uniform loss of follow-up. |
sample size per arm.
# Ex 1. (n_trt=135, n_ctl=135) getSizeOrd(design="parallel", test="equality", alpha=0.05, beta=0.10, varcatprob = list(c(0.2,0.5,0.2,0.1), c(0.378,0.472,0.106,0.044)), k=1, theta=0.887, delta=0, rho=c(0.05, 0.07), r=0.1) # Ex 2. (Check back next version) getSizeOrd(design="crossover", test="equality", alpha=0.05, beta=0.10, varcatprob = list(c(0.2,0.5,0.2,0.1), c(0.378,0.472,0.106,0.044)), k=1, theta=0.887, delta=0, rho=c(0.05, 0.07), r=0.1)# Ex 1. (n_trt=135, n_ctl=135) getSizeOrd(design="parallel", test="equality", alpha=0.05, beta=0.10, varcatprob = list(c(0.2,0.5,0.2,0.1), c(0.378,0.472,0.106,0.044)), k=1, theta=0.887, delta=0, rho=c(0.05, 0.07), r=0.1) # Ex 2. (Check back next version) getSizeOrd(design="crossover", test="equality", alpha=0.05, beta=0.10, varcatprob = list(c(0.2,0.5,0.2,0.1), c(0.378,0.472,0.106,0.044)), k=1, theta=0.887, delta=0, rho=c(0.05, 0.07), r=0.1)
This function computes the sample size required for two arms clinical trials with binary outcome measure. Four hypothesis tests are available under two allocation designs.
getSizeProp( design = c("parallel", "crossover"), test = c("equality", "noninferiority", "superiority", "equivalence"), alpha = 0.05, beta = 0.2, varsigma, k = 1, seqnumber, delta = 0, TTE, rho = c(0.05, 0.07), r = 0.1 )getSizeProp( design = c("parallel", "crossover"), test = c("equality", "noninferiority", "superiority", "equivalence"), alpha = 0.05, beta = 0.2, varsigma, k = 1, seqnumber, delta = 0, TTE, rho = c(0.05, 0.07), r = 0.1 )
design |
allocation method ( |
test |
four hypothesis tests: |
alpha |
level of significance. |
beta |
type II error. |
varsigma |
(varsigma1 > 0, varsigma2 > 0) := (p1, p2) probability of mean response in control and treatment arms; ( |
k |
ratio of control to treatment. |
seqnumber |
Number of crossover sequences: 0 if parallel; 1+ if crossover (seqnumber>=0) |
delta |
delta margin in test hypothesis. |
TTE |
target treatment effect or effect size. |
rho |
vector of length 2, positive noncompliance rates of two arms. |
r |
projected proportion of trial uniform loss of follow-up. |
sample size per arm.
# Ex 1. (n_trt=102, n_ctl=102) getSizeProp(design="parallel", test="equality", alpha=0.05, beta=0.20, varsigma=c(0.65, 0.85), k=1, seqnumber=0, delta=0, TTE=0, rho=c(0.05, 0.07), r=0.1) # Ex 2. (n_trt=33, n_ctl=33) getSizeProp(design="parallel", test="noninferiority", alpha=0.05, beta=0.20, varsigma=c(0.65,0.85), k=1, seqnumber=0, delta=-0.10, TTE=0.20, rho=c(0.05, 0.07), r=0.1) # Ex 3. (n_trt=157, n_ctl=157) getSizeProp(design="parallel", test="superiority", alpha=0.05, beta=0.20, varsigma=c(0.65,0.85), k=1, seqnumber=0, delta=0.05, TTE=0.20, rho=c(0.05, 0.07), r=0.1) # Ex 4. (n_trt=137, n_ctl=137) getSizeProp(design="parallel", test="equivalence", alpha=0.05, beta=0.20, varsigma=c(0.75,0.80), k=1, seqnumber=0, delta=0.20, TTE=0.05, rho=c(0.05, 0.07), r=0.1) # Ex 5. (n_trt=36, n_ctl=36) getSizeProp(design="crossover", test="equality", alpha=0.05, beta=0.20, varsigma=c(0.5,0.5), k=1, seqnumber=2, delta=0, TTE=0.20, rho=c(0.05, 0.07), r=0.1) # Ex 6. (n_trt=22, n_ctl=22) getSizeProp(design="crossover", test="noninferiority", alpha=0.05, beta=0.20, varsigma=c(0.5,0.5), k=1, seqnumber=2, delta=-0.20, TTE=0, rho=c(0.05, 0.07), r=0.1) # Ex 7. (n_trt=86, n_ctl=86) getSizeProp(design="crossover", test="superiority", alpha=0.05, beta=0.20, varsigma=c(0.5,0.5), k=1, seqnumber=2, delta=0.10, TTE=0, rho=c(0.05, 0.07), r=0.1) # Ex 8. (n_trt=30, n_ctl=30) getSizeProp(design="crossover", test="equivalence", alpha=0.05, beta=0.20, varsigma=c(0.5,0.5), k=1, seqnumber=2, delta=0.20, TTE=0, rho=c(0.05, 0.07), r=0.1)# Ex 1. (n_trt=102, n_ctl=102) getSizeProp(design="parallel", test="equality", alpha=0.05, beta=0.20, varsigma=c(0.65, 0.85), k=1, seqnumber=0, delta=0, TTE=0, rho=c(0.05, 0.07), r=0.1) # Ex 2. (n_trt=33, n_ctl=33) getSizeProp(design="parallel", test="noninferiority", alpha=0.05, beta=0.20, varsigma=c(0.65,0.85), k=1, seqnumber=0, delta=-0.10, TTE=0.20, rho=c(0.05, 0.07), r=0.1) # Ex 3. (n_trt=157, n_ctl=157) getSizeProp(design="parallel", test="superiority", alpha=0.05, beta=0.20, varsigma=c(0.65,0.85), k=1, seqnumber=0, delta=0.05, TTE=0.20, rho=c(0.05, 0.07), r=0.1) # Ex 4. (n_trt=137, n_ctl=137) getSizeProp(design="parallel", test="equivalence", alpha=0.05, beta=0.20, varsigma=c(0.75,0.80), k=1, seqnumber=0, delta=0.20, TTE=0.05, rho=c(0.05, 0.07), r=0.1) # Ex 5. (n_trt=36, n_ctl=36) getSizeProp(design="crossover", test="equality", alpha=0.05, beta=0.20, varsigma=c(0.5,0.5), k=1, seqnumber=2, delta=0, TTE=0.20, rho=c(0.05, 0.07), r=0.1) # Ex 6. (n_trt=22, n_ctl=22) getSizeProp(design="crossover", test="noninferiority", alpha=0.05, beta=0.20, varsigma=c(0.5,0.5), k=1, seqnumber=2, delta=-0.20, TTE=0, rho=c(0.05, 0.07), r=0.1) # Ex 7. (n_trt=86, n_ctl=86) getSizeProp(design="crossover", test="superiority", alpha=0.05, beta=0.20, varsigma=c(0.5,0.5), k=1, seqnumber=2, delta=0.10, TTE=0, rho=c(0.05, 0.07), r=0.1) # Ex 8. (n_trt=30, n_ctl=30) getSizeProp(design="crossover", test="equivalence", alpha=0.05, beta=0.20, varsigma=c(0.5,0.5), k=1, seqnumber=2, delta=0.20, TTE=0, rho=c(0.05, 0.07), r=0.1)
This function computes the sample size required for two arms clinical trials with TTE outcome measure. Four hypothesis tests are available under two allocation designs.
getSizeTTE( design = c("parallel", "crossover"), test = c("equality", "noninferiority", "superiority", "equivalence"), alpha = 0.05, beta = 0.2, varlambda, k = 1, ttotal, taccrual, gamma, delta = 0, rho = c(0.05, 0.07), r = 0.1 )getSizeTTE( design = c("parallel", "crossover"), test = c("equality", "noninferiority", "superiority", "equivalence"), alpha = 0.05, beta = 0.2, varlambda, k = 1, ttotal, taccrual, gamma, delta = 0, rho = c(0.05, 0.07), r = 0.1 )
design |
allocation method ( |
test |
four hypothesis tests: |
alpha |
level of significance. |
beta |
type II error. |
varlambda |
(varlambda1>0,varlambda2>0):=(lam1,lam2) hazard rates in control and treatment arms |
k |
ratio of control to treatment. |
ttotal |
total trial time (ttoal>0) |
taccrual |
accrual time period (taccrual>0) |
gamma |
parameter of exponential distribution (gamma>=0) |
delta |
delta margin in test hypothesis. |
rho |
vector of length 2, positive noncompliance rates of two arms. |
r |
projected proportion of trial uniform loss of follow-up. |
sample size per arm.
# Ex 1. (n_trt=56, n_ctl=56) getSizeTTE(design="parallel", test="equality", alpha=0.05, beta=0.20, varlambda=c(1,2), k=1, ttotal=3, taccrual=1, gamma=0.00001, delta=0, rho=c(0.05, 0.07), r=0.1) # Ex 2. (n_trt=30, n_ctl=30) getSizeTTE(design="parallel", test="noninferiority", alpha=0.05, beta=0.20, varlambda=c(1,2), k=1, ttotal=3, taccrual=1, gamma=0.00001, delta= -0.2, rho=c(0.05, 0.07), r=0.1) # Ex 3. (n_trt=74, n_ctl=74) getSizeTTE(design="parallel", test="superiority", alpha=0.05, beta=0.20, varlambda=c(1,2), k=1, ttotal=3, taccrual=1, gamma=0.00001, delta=0.20, rho=c(0.05, 0.07), r=0.1) # Ex 4. (n_trt=84, n_ctl=84) getSizeTTE(design="parallel", test="equivalence", alpha=0.05, beta=0.20, varlambda=c(1,1), k=1, ttotal=3, taccrual=1, gamma=0.00001, delta=0.5, rho=c(0.05, 0.07), r=0.1) # Ex 5. (Check back next version) getSizeTTE(design="crossover", test="equality", alpha=0.05, beta=0.20, varlambda=c(1,1), k=1, ttotal=3, taccrual=1, gamma=0.00001, delta=0.5, rho=c(0.05, 0.07), r=0.1) # Ex 6. (Check back next version) getSizeTTE(design="crossover", test="noninferiority", alpha=0.05, beta=0.20, varlambda=c(1,1), k=1, ttotal=3, taccrual=1, gamma=0.00001, delta=0.5, rho=c(0.05, 0.07), r=0.1) # Ex 7. (Check back next version) getSizeTTE(design="crossover", test="superiority", alpha=0.05, beta=0.20, varlambda=c(1,1), k=1, ttotal=3, taccrual=1, gamma=0.00001, delta=0.5, rho=c(0.05, 0.07), r=0.1) # Ex 8. (Check back next version) getSizeTTE(design="crossover", test="equivalence", alpha=0.05, beta=0.20, varlambda=c(1,1), k=1, ttotal=3, taccrual=1, gamma=0.00001, delta=0.5, rho=c(0.05, 0.07), r=0.1)# Ex 1. (n_trt=56, n_ctl=56) getSizeTTE(design="parallel", test="equality", alpha=0.05, beta=0.20, varlambda=c(1,2), k=1, ttotal=3, taccrual=1, gamma=0.00001, delta=0, rho=c(0.05, 0.07), r=0.1) # Ex 2. (n_trt=30, n_ctl=30) getSizeTTE(design="parallel", test="noninferiority", alpha=0.05, beta=0.20, varlambda=c(1,2), k=1, ttotal=3, taccrual=1, gamma=0.00001, delta= -0.2, rho=c(0.05, 0.07), r=0.1) # Ex 3. (n_trt=74, n_ctl=74) getSizeTTE(design="parallel", test="superiority", alpha=0.05, beta=0.20, varlambda=c(1,2), k=1, ttotal=3, taccrual=1, gamma=0.00001, delta=0.20, rho=c(0.05, 0.07), r=0.1) # Ex 4. (n_trt=84, n_ctl=84) getSizeTTE(design="parallel", test="equivalence", alpha=0.05, beta=0.20, varlambda=c(1,1), k=1, ttotal=3, taccrual=1, gamma=0.00001, delta=0.5, rho=c(0.05, 0.07), r=0.1) # Ex 5. (Check back next version) getSizeTTE(design="crossover", test="equality", alpha=0.05, beta=0.20, varlambda=c(1,1), k=1, ttotal=3, taccrual=1, gamma=0.00001, delta=0.5, rho=c(0.05, 0.07), r=0.1) # Ex 6. (Check back next version) getSizeTTE(design="crossover", test="noninferiority", alpha=0.05, beta=0.20, varlambda=c(1,1), k=1, ttotal=3, taccrual=1, gamma=0.00001, delta=0.5, rho=c(0.05, 0.07), r=0.1) # Ex 7. (Check back next version) getSizeTTE(design="crossover", test="superiority", alpha=0.05, beta=0.20, varlambda=c(1,1), k=1, ttotal=3, taccrual=1, gamma=0.00001, delta=0.5, rho=c(0.05, 0.07), r=0.1) # Ex 8. (Check back next version) getSizeTTE(design="crossover", test="equivalence", alpha=0.05, beta=0.20, varlambda=c(1,1), k=1, ttotal=3, taccrual=1, gamma=0.00001, delta=0.5, rho=c(0.05, 0.07), r=0.1)