Description Usage Arguments Details Value Author(s) References See Also Examples
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.
1 2 3 4 5 | 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 |
A vector of user-specified time points (B1, ..., Bb) that determine a set of time intervals with uniform accrual. |
m.init |
The projected
number of patients that can be accrued within the time intervals determined by |
alpha |
Type I error. |
beta |
Type II error. |
param |
Events should be defined as poor outcomes (e.g. death, progression). Computations
and reporting are based on the proportion without an event at a
pre-specified time, |
x |
Pre-specified time for the event-free endpoint (e.g., 1 year). |
n |
User-specified combined sample size for both stages. |
pn |
Combined sample size for both stages specified by the ratio of the targetted two-stage sample size to the correponding sample size for a single-stage design. |
pt |
Combined sample size for both stages specified by the ratio of the targetted two-stage study length to the correponding study length for a single-stage design. |
target |
The expected duration of
accrual (EDA) is minimized with |
sf |
Spending function for |
num.arm |
Number of arms: a single-arm design with |
r |
Proportion of patients randomized to the treatment arm when |
num.stage |
Number of stages: a two-stage design with |
pause |
The pause in accrual following the scheduled times for interim
analyses. Data collected during the pause on the previously accrued
patients are included in the interim analysis conducted at the end of the
pause. Accrual resumes after the pause without interuption as if no pause had
occurred. Default is |
control |
An optional list of control settings. See
|
... |
No additional optional parameters are currently implemented |
Plots (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.
A list of class OptimDes
with the same output as function OptimDes
.
Bo Huang <bo.huang@pfizer.com> and Neal Thomas <neal.thomas@pfizer.com>
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
1 2 3 4 5 6 7 8 9 10 11 12 13 |
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