get_stagewise_estimators: Conditional representations of an estimator or p-value

In adestr: Estimation in Optimal Adaptive Two-Stage Designs

Conditional representations of an estimator or p-value

Description

This generic determines the functional representations of point and interval estimators and p-values. The functions are returned in two parts, one part to calculate the values conditional on early futility or efficacy stops (i.e. where no second stage mean and sample size is available), and one conditional on continuation to the second stage.

Usage

get_stagewise_estimators(
  estimator,
  data_distribution,
  use_full_twoarm_sampling_distribution = FALSE,
  design,
  sigma,
  exact = FALSE
)

## S4 method for signature 'VirtualPointEstimator,ANY'
get_stagewise_estimators(
  estimator,
  data_distribution,
  use_full_twoarm_sampling_distribution = FALSE,
  design,
  sigma,
  exact = FALSE
)

## S4 method for signature 'VirtualPValue,ANY'
get_stagewise_estimators(
  estimator,
  data_distribution,
  use_full_twoarm_sampling_distribution = FALSE,
  design,
  sigma,
  exact = FALSE
)

## S4 method for signature 'VirtualIntervalEstimator,ANY'
get_stagewise_estimators(
  estimator,
  data_distribution,
  use_full_twoarm_sampling_distribution = FALSE,
  design,
  sigma,
  exact = FALSE
)

## S4 method for signature 'PointEstimator,Student'
get_stagewise_estimators(
  estimator,
  data_distribution,
  use_full_twoarm_sampling_distribution = FALSE,
  design,
  sigma,
  exact = FALSE
)

## S4 method for signature 'PValue,Student'
get_stagewise_estimators(
  estimator,
  data_distribution,
  use_full_twoarm_sampling_distribution = FALSE,
  design,
  sigma,
  exact = FALSE
)

## S4 method for signature 'IntervalEstimator,Student'
get_stagewise_estimators(
  estimator,
  data_distribution,
  use_full_twoarm_sampling_distribution = FALSE,
  design,
  sigma,
  exact = FALSE
)

## S4 method for signature 'VirtualPointEstimator,Student'
get_stagewise_estimators(
  estimator,
  data_distribution,
  use_full_twoarm_sampling_distribution = FALSE,
  design,
  sigma,
  exact = FALSE
)

## S4 method for signature 'VirtualIntervalEstimator,Student'
get_stagewise_estimators(
  estimator,
  data_distribution,
  use_full_twoarm_sampling_distribution = FALSE,
  design,
  sigma,
  exact = FALSE
)

## S4 method for signature 'VirtualPValue,Student'
get_stagewise_estimators(
  estimator,
  data_distribution,
  use_full_twoarm_sampling_distribution = FALSE,
  design,
  sigma,
  exact = FALSE
)

## S4 method for signature 'PointEstimator,DataDistribution'
get_stagewise_estimators(
  estimator,
  data_distribution,
  use_full_twoarm_sampling_distribution = FALSE,
  design,
  sigma,
  exact = FALSE
)

## S4 method for signature 'PValue,DataDistribution'
get_stagewise_estimators(
  estimator,
  data_distribution,
  use_full_twoarm_sampling_distribution = FALSE,
  design,
  sigma,
  exact = FALSE
)

## S4 method for signature 'IntervalEstimator,DataDistribution'
get_stagewise_estimators(
  estimator,
  data_distribution,
  use_full_twoarm_sampling_distribution = FALSE,
  design,
  sigma,
  exact = FALSE
)

## S4 method for signature 'AdaptivelyWeightedSampleMean,Normal'
get_stagewise_estimators(
  estimator,
  data_distribution,
  use_full_twoarm_sampling_distribution = FALSE,
  design,
  sigma,
  exact = FALSE
)

## S4 method for signature 'MinimizePeakVariance,Normal'
get_stagewise_estimators(
  estimator,
  data_distribution,
  use_full_twoarm_sampling_distribution = FALSE,
  design,
  sigma,
  exact = FALSE
)

## S4 method for signature 'BiasReduced,Normal'
get_stagewise_estimators(
  estimator,
  data_distribution,
  use_full_twoarm_sampling_distribution = FALSE,
  design,
  sigma,
  exact = FALSE
)

## S4 method for signature 'RaoBlackwell,Normal'
get_stagewise_estimators(
  estimator,
  data_distribution,
  use_full_twoarm_sampling_distribution = FALSE,
  design,
  sigma,
  exact = FALSE
)

## S4 method for signature 'PseudoRaoBlackwell,Normal'
get_stagewise_estimators(
  estimator,
  data_distribution,
  use_full_twoarm_sampling_distribution = FALSE,
  design,
  sigma,
  exact = FALSE
)

## S4 method for signature 'RepeatedCI,Normal'
get_stagewise_estimators(
  estimator,
  data_distribution,
  use_full_twoarm_sampling_distribution = FALSE,
  design,
  sigma,
  exact = FALSE
)

## S4 method for signature 'LinearShiftRepeatedPValue,Normal'
get_stagewise_estimators(
  estimator,
  data_distribution,
  use_full_twoarm_sampling_distribution = FALSE,
  design,
  sigma,
  exact = FALSE
)

## S4 method for signature 'MLEOrderingPValue,Normal'
get_stagewise_estimators(
  estimator,
  data_distribution,
  use_full_twoarm_sampling_distribution = FALSE,
  design,
  sigma,
  exact = FALSE
)

## S4 method for signature 'LikelihoodRatioOrderingPValue,Normal'
get_stagewise_estimators(
  estimator,
  data_distribution,
  use_full_twoarm_sampling_distribution = FALSE,
  design,
  sigma,
  exact = FALSE
)

## S4 method for signature 'ScoreTestOrderingPValue,Normal'
get_stagewise_estimators(
  estimator,
  data_distribution,
  use_full_twoarm_sampling_distribution = FALSE,
  design,
  sigma,
  exact = FALSE
)

## S4 method for signature 'StagewiseCombinationFunctionOrderingPValue,Normal'
get_stagewise_estimators(
  estimator,
  data_distribution,
  use_full_twoarm_sampling_distribution = FALSE,
  design,
  sigma,
  exact = FALSE
)

## S4 method for signature 'NeymanPearsonOrderingPValue,Normal'
get_stagewise_estimators(
  estimator,
  data_distribution,
  use_full_twoarm_sampling_distribution = FALSE,
  design,
  sigma,
  exact = FALSE
)

## S4 method for signature 'NaivePValue,Normal'
get_stagewise_estimators(
  estimator,
  data_distribution,
  use_full_twoarm_sampling_distribution = FALSE,
  design,
  sigma,
  exact = FALSE
)

## S4 method for signature 'StagewiseCombinationFunctionOrderingCI,Normal'
get_stagewise_estimators(
  estimator,
  data_distribution,
  use_full_twoarm_sampling_distribution = FALSE,
  design,
  sigma,
  exact = FALSE
)

## S4 method for signature 'MLEOrderingCI,Normal'
get_stagewise_estimators(
  estimator,
  data_distribution,
  use_full_twoarm_sampling_distribution = FALSE,
  design,
  sigma,
  exact = FALSE
)

## S4 method for signature 'LikelihoodRatioOrderingCI,Normal'
get_stagewise_estimators(
  estimator,
  data_distribution,
  use_full_twoarm_sampling_distribution = FALSE,
  design,
  sigma,
  exact = FALSE
)

## S4 method for signature 'ScoreTestOrderingCI,Normal'
get_stagewise_estimators(
  estimator,
  data_distribution,
  use_full_twoarm_sampling_distribution = FALSE,
  design,
  sigma,
  exact = FALSE
)

## S4 method for signature 'NeymanPearsonOrderingCI,Normal'
get_stagewise_estimators(
  estimator,
  data_distribution,
  use_full_twoarm_sampling_distribution = FALSE,
  design,
  sigma,
  exact = FALSE
)

## S4 method for signature 'NaiveCI,Normal'
get_stagewise_estimators(
  estimator,
  data_distribution,
  use_full_twoarm_sampling_distribution = FALSE,
  design,
  sigma,
  exact = FALSE
)

## S4 method for signature 
## 'MidpointStagewiseCombinationFunctionOrderingCI,Normal'
get_stagewise_estimators(
  estimator,
  data_distribution,
  use_full_twoarm_sampling_distribution = FALSE,
  design,
  sigma,
  exact = FALSE
)

## S4 method for signature 'MidpointMLEOrderingCI,Normal'
get_stagewise_estimators(
  estimator,
  data_distribution,
  use_full_twoarm_sampling_distribution = FALSE,
  design,
  sigma,
  exact = FALSE
)

## S4 method for signature 'MidpointLikelihoodRatioOrderingCI,Normal'
get_stagewise_estimators(
  estimator,
  data_distribution,
  use_full_twoarm_sampling_distribution = FALSE,
  design,
  sigma,
  exact = FALSE
)

## S4 method for signature 'MidpointScoreTestOrderingCI,Normal'
get_stagewise_estimators(
  estimator,
  data_distribution,
  use_full_twoarm_sampling_distribution = FALSE,
  design,
  sigma,
  exact = FALSE
)

## S4 method for signature 'MidpointNeymanPearsonOrderingCI,Normal'
get_stagewise_estimators(
  estimator,
  data_distribution,
  use_full_twoarm_sampling_distribution = FALSE,
  design,
  sigma,
  exact = FALSE
)

## S4 method for signature 
## 'MedianUnbiasedStagewiseCombinationFunctionOrdering,Normal'
get_stagewise_estimators(
  estimator,
  data_distribution,
  use_full_twoarm_sampling_distribution = FALSE,
  design,
  sigma,
  exact = FALSE
)

## S4 method for signature 'MedianUnbiasedMLEOrdering,Normal'
get_stagewise_estimators(
  estimator,
  data_distribution,
  use_full_twoarm_sampling_distribution = FALSE,
  design,
  sigma,
  exact = FALSE
)

## S4 method for signature 'MedianUnbiasedLikelihoodRatioOrdering,Normal'
get_stagewise_estimators(
  estimator,
  data_distribution,
  use_full_twoarm_sampling_distribution = FALSE,
  design,
  sigma,
  exact = FALSE
)

## S4 method for signature 'MedianUnbiasedScoreTestOrdering,Normal'
get_stagewise_estimators(
  estimator,
  data_distribution,
  use_full_twoarm_sampling_distribution = FALSE,
  design,
  sigma,
  exact = FALSE
)

## S4 method for signature 'MedianUnbiasedNeymanPearsonOrdering,Normal'
get_stagewise_estimators(
  estimator,
  data_distribution,
  use_full_twoarm_sampling_distribution = FALSE,
  design,
  sigma,
  exact = FALSE
)

Arguments

estimator	object of class PointEstimator, IntervalEstimator or PValue.
data_distribution	object of class Normal or Student.
use_full_twoarm_sampling_distribution	logical indicating whether this estimator is intended to be used with the full sampling distribution in a two-armed trial.
design	object of class TwoStageDesign.
sigma	assumed standard deviation.
exact	logical indicating usage of exact n2 function.

Value

a list with the conditional functional representations (one for each stage where the trial might end) of the estimator or p-value.

Examples

get_stagewise_estimators(
  estimator = SampleMean(),
  data_distribution = Normal(FALSE),
  use_full_twoarm_sampling_distribution = FALSE,
  design = get_example_design(),
  sigma = 1,
  exact = FALSE
)

adestr documentation built on Sept. 11, 2024, 6:05 p.m.