OptimDes(
B.init, m.init, alpha, beta, param, x, target = c("EDA", "ETSL","ES"),
sf=c("futility","OF","Pocock"), num.arm,r=0.5, num.stage=2,
pause=0,
control = OptimDesControl(),...)B.init. A large number of potential patients
results in long execution times for OptimDes, so
unrealistically large values should not be entered.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", or 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.alpha, type II
error beta, Weibull parameters param and survival time
of interest x.num.arm,
treatment randomization rate r, the number of study stages num.stage,
the pause in accrual before an interim analysis pause.B.init and
m.init.Test2stage)
for the test statistic and decision rules.x). Exposure is a vector of length 1 or 2. The
first value is the expected exposure
at the first interim analysis. For two-stage, single-group designs,
the second value is exposure with the Case-Morgan finite sample
adjustment. For 3-stage designs, the second value
is the exposure at the second interim analysis. For two-stage, two-group designs,
exposure is a scalar indicating the expected exposure at the first interim analysis.fix.n, duration of accrual DA, study length
SL, and corresponding values based on the exact binomial test for a one-arm single-stage design
and the Fisher exact test for a two-arm single-stage design with the design distributional assumptions. OptimDes finds an two-stage or three-stage design with a time to event endpoint
evaluated at a pre-specified time with potential stopping after the first stage.
For single arm designs, it implements the Case and Morgan (2003) and Huang, Talukder and Thomas (2010) generalizaton of the Simon (1989) two-stage design for comparing a treatment to a known standard rate with possible stopping for futility at the interim.
For randomized two-arm comparative designs, it allows an early stopping for both
futility and superiority. The spending function for superiority can be chosen with
argument sf.
The design minimizes either the expected duration of accrual (EDA), expected sample size (ES), or the expected total study length (ETSL).
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. Estimation is based on the Kaplan-Meier or
Nelson-Aalen estimators evaluated at a target time (e.g., 1 year).
The full treatment and control distributions and the accrual
distribution affect power (and alpha level in some settings), see Case and Morgan (2003)).
Accrual rates are specified by the user. These rates can differ across time intervals specified by the user (this generalizes the results in Case and Morgan).
A package vignette as user manual can be found in
the /doc subdirectory of the OptInterim package. It can be accessed from the
HTML help page for the package.
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.
np.OptimDes, print.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
#
# object12 <- OptimDes(B.init,m.init,alpha,beta,param,x,target="EDA",
# sf="futility",num.arm=1,num.stage=2,control=OptimDesControl(n.int=c(1,5),trace=TRUE))
# print(object12)
#
#
# m.init <- 4*c(15, 20, 25, 20, 15)
# object2 <- OptimDes(B.init,m.init,alpha,beta,param,x,target="EDA",sf="futility",num.arm=2)
# print(object2)
#
# object23O <- OptimDes(B.init,m.init,alpha,beta,param,x,target="ETSL",sf="OF",
# num.arm=2,num.stage=3,control=OptimDesControl(trace=TRUE,aboveMin=c(1.05,1.10)))
# print(object3)
# ## End(Not run)
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