TwoStageDesign-class R Documentation

## Two-stage designs

### Description

`TwoStageDesign` is the fundamental design class of the adoptr package. Formally, we represent a generic two-stage design as a five-tuple (n1, c1f, c1e, n2(·), c2(·)). Here, n1 is the first-stage sample size (per group), c1f and c1e are boundaries for early stopping for futility and efficacy, respectively. Since the trial design is a two-stage design, the elements n2(·) (stage-two sample size) and c2(·) (stage-two critical value) are functions of the first-stage outcome X1=x1. X1 denotes the first-stage test statistic. A brief description on this definition of two-stage designs can be read here. For available methods, see the 'See Also' section at the end of this page.

### Usage

``````TwoStageDesign(n1, ...)

## S4 method for signature 'numeric'
TwoStageDesign(n1, c1f, c1e, n2_pivots, c2_pivots, order = NULL, ...)

## S4 method for signature 'TwoStageDesign'
summary(object, ..., rounded = TRUE)
``````

### Arguments

 `n1` stage-one sample size `...` further optional arguments `c1f` early futility stopping boundary `c1e` early efficacy stopping boundary `n2_pivots` numeric vector, stage-two sample size on the integration pivot points `c2_pivots` numeric vector, stage-two critical values on the integration pivot points `order` `integer`, integration order of the employed Gaussian quadrature integration rule to evaluate scores. Automatically set to `length(n2_pivots)` if `length(n2_pivots) == length(c2_pivots) > 1`, otherwise c2 and n2 are taken to be constant in stage-two and replicated to match the number of pivots specified by `order` `object` object to show `rounded` should rounded n-values be used?

### Details

`summary` can be used to quickly compute and display basic facts about a TwoStageDesign. An arbitrary number of names `UnconditionalScore` objects can be provided via the optional arguments `...` and are included in the summary displayed using `print`.

### Slots

`n1`

cf. parameter 'n1'

`c1f`

cf. parameter 'c1f'

`c1e`

cf. parameter 'c1e'

`n2_pivots`

vector of length 'order' giving the values of n2 at the pivot points of the numeric integration rule

`c2_pivots`

vector of length order giving the values of c2 at the pivot points of the numeric integration rule

`x1_norm_pivots`

normalized pivots for integration rule (in [-1, 1]) the actual pivots are scaled to the interval [c1f, c1e] and can be obtained by the internal method
`adoptr:::scaled_integration_pivots(design)`

`weights`

weights of of integration rule at `x1_norm_pivots` for approximating integrals over `x1`

`tunable`

named logical vector indicating whether corresponding slot is considered a tunable parameter (i.e. whether it can be changed during optimization via `minimize` or not; cf.
`make_fixed`)

For accessing sample sizes and critical values safely, see methods in `n` and `c2`; for modifying behaviour during optimizaton see `make_tunable`; to convert between S4 class represenation and numeric vector, see `tunable_parameters`; for simulating from a given design, see `simulate`; for plotting see `plot,TwoStageDesign-method`. Both group-sequential and one-stage designs (!) are implemented as subclasses of `TwoStageDesign`.

### Examples

``````design <- TwoStageDesign(50, 0, 2, 50.0, 2.0, 5)
pow    <- Power(Normal(), PointMassPrior(.4, 1))
summary(design, "Power" = pow)

``````

adoptr documentation built on June 22, 2024, 9:21 a.m.