Provide early termination phase II trial designs with a decreasingly informative prior (DIP) or a regular Bayesian prior chosen by the user. The program can determine the minimum planned sample size necessary to achieve the user-specified admissible designs. The program can also perform power and expected sample size calculations for the tests in early termination Phase II trials.[1]
You can install from CRAN with:
install.packages("BayesDIP")Or try the development version from [GitHub] with:
# install.packages("devtools")
devtools::install_github("chenw10/BayesDIP")library(BayesDIP)
# Calculate the minimum planned sample size within the range 10<=N<=100,
# under an admissible design which is set as 80% power and 5% type I error here.
# One sample Bernoulli model with the response rate for the new treatment is 0.5,
# the null response rate is 0.3, and the target improvement to achieve is 0.
# The alternative hypothesis: p1 > p0 + d
# Simulate 10 replicate trials using this design with efficacy boundary 0.98
# and futility boundary 0.05.
### Designs with traditional Bayesian prior Beta(1,1)
### Designs and operating characteristics based on 100 simulations:
OneSampleBernoulli.Design(list(2,1,1), nmin = 10, nmax=100, p0 = 0.3, p1 = 0.5, d = 0,
ps = 0.98, pf = 0.02, power = 0.80, t1error=0.05, alternative = "greater",
seed = 202210, sim = 100)
#>
#> Prior: Beta(1,1)
#> Planned Sample Size: 92
#> Efficacy Boundary: 0.98
#> Futility Boundary: 0.02
#> Exact Power: 0.99
#> Exact Type I error: 0.05
#> Expected sample size: 24
#> Expected sample size standard deviation: 17.6
### Designs with DIP
### Designs and operating characteristics based on 10 simulations:
OneSampleBernoulli.Design(list(1,0,0), nmin = 10, nmax=100, p0 = 0.3, p1 = 0.5, d = 0,
ps = 0.98, pf = 0.02, power = 0.80, t1error=0.05, alternative = "greater",
seed = 202210, sim = 100)
#>
#> Prior: DIP
#> Planned Sample Size: 44
#> Efficacy Boundary: 0.98
#> Futility Boundary: 0.02
#> Exact Power: 0.81
#> Exact Type I error: 0.05
#> Expected sample size: 30
#> Expected sample size standard deviation: 9.7
# Calculate the power, type I error and the expected sample size given a planned sample size
# One sample Bernoulli model with the response rate for the new treatment is 0.5,
# the null response rate is 0.3, and the target improvement to achieve is 0.05.
# The alternative hypothesis: p1 > p0 + d
# Simulate 100 replicate trials for a given planned sample size 100 using this design
# with efficacy boundary 0.98 and futility boundary 0.05.
## with traditional Bayesian prior Beta(1,1)
## Operating characteristics based on 100 simulations:
OneSampleBernoulli(list(2,1,1), N = 100, p0 = 0.3, p1 = 0.5, d = 0.05,
ps = 0.98, pf = 0.05, alternative = "greater",
seed = 202210, sim = 100)
#>
#> Prior: Beta(1,1)
#> Power: 0.89
#> Type I error: 0.05
#> Expected sample size: 42.7
#> Expected sample size standard deviation: 29.06
#> The probability of reaching the efficacy boundary: 0.89
#> The probability of reaching the futility boundary: 0.02
## with DIP
## Operating characteristics based on 100 simulations:
OneSampleBernoulli(list(1,0,0), N = 100, p0 = 0.3, p1 = 0.5, d = 0.05,
ps = 0.98, pf = 0.05, alternative = "greater",
seed = 202210, sim = 100)
#>
#> Prior: DIP
#> Power: 0.86
#> Type I error: 0.01
#> Expected sample size: 72.1
#> Expected sample size standard deviation: 17.28
#> The probability of reaching the efficacy boundary: 0.86
#> The probability of reaching the futility boundary: 0[1] Wang C, Sabo RT, Mukhopadhyay ND, and Perera RA. Early termination in single-parameter model phase II clinical trial designs using decreasingly informative priors. , 9(2): April - June 2022. https://doi.org/10.18203/2349-3259.ijct20221110