pval_gs: Determine p-values in a group sequential single-arm trial...
In mjg211/singlearm: Design and analysis of single-arm clinical trials

Description Usage Arguments Details Value See Also Examples

Determines possible p-values at the end of a group sequential single-arm trial for a single binary endpoint, as determined using des_gs(). Support is available to compute point estimates using the naive ("naive"), MLE-ordering ("mle"), UMVUE-ordering ("umvue"), and conditional ("conditional") approaches.

1 2	pval_gs(des, k, pi, method = c("conditional", "mle", "naive", "umvue"), summary = F)

`des`	An object of class `"sa_des_gs"`, as returned by `des_gs()`.
`k`	Calculations are performed conditional on the trial stopping in one of the stages listed in vector `k`. Thus, `k` should be a vector of integers, with elements between one and the maximum number of allowed stages across the supplied designs. If left unspecified, it will internally default to all possible stages.
`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 `des` if it is left unspecified.
`method`	A vector of methods to use to construct point estimates. Currently, support is available to use the naive (`"naive"`), MLE-ordering (`"mle"`), UMVUE-ordering (`"umvue"`), and conditional (`"conditional"`), approaches. Defaults to all available methods.
`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 p-value calculation 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_pval_gs" containing the following elements

A tibble in the slot $pval summarising the possible p-values 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 p-value calculation procedures for each specified value of π.
Each of the input variables as specified, subject to internal modification.

des_gs, opchar_gs, est_gs, ci_gs, and their associated plot family of functions.

# Find the optimal two-stage design for the default parameters
des  <- des_gs()
# Determine the performance of all supported p-value calculation procedures
# for a range of possible response probabilities
pval <- pval_gs(des, pi = seq(0, 1, 0.01))