Description Usage Arguments Details Value See Also Examples
View source: R/est_curtailed.R
Determines possible point estimates at the end of a curtailed group
sequential single-arm trial for a single binary endpoint, as determined using
des_curtailed()
. Support is available to compute point estimates using
the naive ("naive"
), bias-adjusted ("bias_adj"
),
bias-subtracted ("bias_sub"
), conditional ("conditional"
),
median unbiased ("mue"
), and UMVUE ("umvue"
) approaches.
1 2 | est_curtailed(des, k, pi, method = c("bias_adj", "bias_sub",
"conditional", "naive", "mue", "umvue"), summary = F)
|
des |
An object of class |
k |
Calculations are performed conditional on the trial stopping in one
of the stages listed in vector |
pi |
A vector of response probabilities to evaluate the expected
performance of the point estimation procedures at. This will internally
default to be the π0
and π1 from
|
method |
A vector of methods to use to construct point estimates.
Currently, support is available to use the naive ( |
summary |
A logical variable indicating whether a summary of the function's progress should be printed to the console. |
In addition, the performance of the chosen point estimate procedures (including their expected value and variance) for each value of pi in the supplied vector pi, will also be evaluated.
Calculations are performed conditional on the trial stopping in one of the
stages specified using the input (vector) k
.
A list of class "sa_est_curtailed"
containing the following
elements
A tibble in the slot $est
summarising the possible point
estimates at the end of the trial for the supplied design, according to the
chosen methods.
A tibble in the slot $perf
summarising the performance of the
chosen point estimation procedures for each specified value of
π.
Each of the input variables as specified, subject to internal modification.
des_curtailed
, opchar_curtailed
,
pval_curtailed
, ci_curtailed
, and their
associated plot
family of functions.
1 2 3 4 5 6 | # Find the optimal non-stochastically curtailed two-stage design for the
default parameters
des <- des_curtailed()
# Determine the performance of all supported point estimation procedures for
# a range of possible response probabilities
est <- est_gs(des, pi = seq(0, 1, 0.01))
|
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