des_ss_norm | R Documentation |
des_ss_norm()
determines single-stage multi-arm clinical trial designs
assuming the primary outcome variable is normally distributed. It supports a
variety of multiple comparison corrections, along with the determination of
A-, D-, and E-optimal allocation ratios. In all
instances, des_ss_norm()
computes the required sample size in each
arm, and returns information on key operating characteristics.
des_ss_norm(
K = 2,
alpha = 0.025,
beta = 0.1,
delta1 = 0.5,
delta0 = 0,
sigma = rep(1, K + 1),
ratio = rep(1, K),
correction = "dunnett",
power = "marginal",
integer = FALSE,
summary = FALSE
)
K |
A |
alpha |
A |
beta |
A |
delta1 |
A |
delta0 |
A |
sigma |
A |
ratio |
Either a |
correction |
A |
power |
A |
integer |
A |
summary |
A |
A list
, with additional class
"multiarm_des_ss_norm"
, containing the following elements:
A tibble
in the slot $opchar
summarising the
operating characteristics of the identified design.
A numeric
in the slot $N
specifying
N, the trial's total required sample
size.
A numeric
vector in the slot $n
specifying
n, the vector of sample
sizes required in each arm.
A numeric
in the slot $gamma
specifying the
critical threshold for p-values,
γ, below which null
hypotheses would be rejected. Will be NA
if correction
is not a single-step testing procedure.
A numeric
vector in the slot $gammaO
specifying
the critical thresholds for ordered p-values,
γ, to use with
the chosen step-wise testing procedure. Will be NA
if
correction
is not a step-wise testing procedure.
A matrix
in the slot $CovZ
specifying the
covariance matrix,
Cov(Z), of the
standardised test statistics.
Each of the input variables.
build_ss_norm
, gui
,
opchar_ss_norm
, plot.multiarm_des_ss_norm
,
sim_ss_norm
.
# The design for the default parameters
des <- des_ss_norm()
# An A-optimal design
des_A <- des_ss_norm(ratio = "A")
# Using the root-K allocation rule, modifying the desired type of power, and
# choosing an alternative multiple comparison correction
des_root_K <- des_ss_norm(ratio = rep(1/sqrt(2), 2),
correction = "holm_bonferroni",
power = "disjunctive")
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