Construct a two-stage or three-stage design with a time-to-event endpoint evaluated at a pre-specified time (e.g., 6-month progression-free survival) comparing treatment versus either a historical control rate with possible stopping for futility (single-arm), or an active control arm with possible stopping for both futility and superiority (two-arm), after the end of Stage 1 utilizing time to event data. The design minimizes either the expected duration of accrual (EDA), the expected sample size (ES), or the expected total study length (ETSL). The maximum combined sample size for both stages is pre-specifed by the user.
np.OptimDes( B.init, m.init, alpha, beta, param, x, n = NULL, pn = NULL, pt = NULL, target = c("EDA", "ETSL","ES"), sf=c("futility","OF","Pocock"), num.arm,r=0.5,num.stage=2,pause=0, control = OptimDesControl(), ...)B.init.x. For constructing an optimal design,
complete event-free distributions at all times must be specified for
the control condition (Null), and for
the alternative "effective" treatment. Weibull distributions are currently
implemented. param is a vector of length 4: (shape null,
scale null, shape alternative, scale alternative). The R parameterization of the Weibull
distribution is used.target="EDA", the
expected total study length is minimized with
target="ETSL", and the expected sample size with target="ES".alpha at the end of Stage 1. There are three
types of spending functions: no efficacy stopping with sf="futility",
O'Brien-Fleming boundaries with sf="OF", and Pocock boundaries with
sf="Pocock".num.arm=1, or a
randomized two-arm design with num.arm=2.num.arm=2.
By default, r=0.5.num.stage=2, or a
three-stage design with num.stage=3.pause=0.OptimDesControl
for the parameters that can be set and their default values.OptimDes with the same output as function OptimDes.
plot.OptimDes) based on the ouput of
OptimDes can be used to find compromise designs based on
different combined sample sizes with
near optimal values for both ETSL ES, and EDA. np.OptimDes can be
used to compute ETSL, ES, EDA, and the other design parameters for any
specified total sample size.The targeted combined sample size must be specified by one of
three equivalent approaches: n, pn, and pt.
The design calculations assume Weibull distributions for the event-free
endpoint in the treatment group, and for the (assumed known, "Null") control
distribution.
The function weibPmatch can be used to select
Weibull parameters that yield a target event-free rate at a
specified time.
Huang B., Talukder E. and Thomas N. (2010). Optimal two-stage Phase II designs with long-term endpoints. Statistics in Biopharmaceutical Research, 2, 51--61. Case M. D. and Morgan T. M. (2003) Design of Phase II cancer trials evaluating survival probabilities. BMC Medical Research Methodology, 3, 7.
Lin D. Y., Shen L., Ying Z. and Breslow N. E. (1996) Group seqential designs for monitoring survival probabilities. Biometrics, 52, 1033--1042.
Simon R. (1989) Optimal two-stage designs for phase II clinical trials. Controlled Clinical Trials, 10, 1--10.
OptimDes, plot.OptimDes,
weibPmatch## Not run:
# B.init <- c(1, 2, 3, 4, 5)
# m.init <- c(15, 20, 25, 20, 15)
# alpha <- 0.05
# beta <- 0.1
# param <- c(1, 1.09, 2, 1.40)
# x <- 1
#
# # H0: S0=0.40 H1: S1=0.60
#
# object14 <- np.OptimDes(B.init,m.init,alpha,beta,param,x,pt=1.1,target="ETSL",sf="futility",num.arm=1,num.stage=2)
# ## End(Not run)
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