Description Usage Arguments Value See Also Examples
des_gs()
determines (non-optimised) group-sequential clinical trial
designs assuming the primary outcome variable is normally distributed. It
supports a variety of popular boundary shapes: Haybittle-Peto, power-family,
triangular, and Wang-Tsiatis (which includes O'Brien-Fleming and Pocock)
designs.
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J |
A |
alpha |
A |
beta |
A |
delta |
A |
sigma0 |
A |
sigma1 |
A |
ratio |
A |
shape |
A |
Delta |
Only used if |
quantile_sub |
A |
integer_n |
A |
summary |
A |
A list
with additional class "OptGS_des"
. It
will contain each of the input variables (subject to internal modification),
relating them to the outputs of the various group-sequential design functions
in OptGS
, along with additional elements including:
CovZ
: A numeric
matrix
giving
Cov(Z), the
covariance between the standardised test statistics for the identified
design.
e
: A numeric
vector
giving
e, the efficacy stopping
boundaries for the identified design.
f
: A numeric
vector
giving
f, the futility stopping
boundaries for the identified design.
I
: A numeric
vector
giving
I, the vector of
information levels for the identified design.
n
: A numeric
vector
giving
n, the vector of
stage-wise sample sizes for the identified design.
n_fixed
: A numeric
giving the sample size required
by a corresponding fixed-sample design.
n0
: A numeric
giving
n0, the group size in the
control arm for the identified design.
n1
: A numeric
giving
n1, the group size in the
experimental arm for the identified design.
name
: A character
string giving a name for the
identified design.
opchar
: A tibble
giving the operating
characteristics of the identified design when
τ = 0,
τ = δ, and
τ =
argmaxθESS(θ).
build
, des_nearopt
,
des_opt
, est
, opchar
,
sim
, plot.OptGS_des
,
print.OptGS_des
, summary.OptGS_des
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