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Conditional representations of an estimator or p-value

get_stagewise_estimators.Rd

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 class 'VirtualPointEstimator,ANY'
get_stagewise_estimators(
  estimator,
  data_distribution,
  use_full_twoarm_sampling_distribution = FALSE,
  design,
  sigma,
  exact = FALSE
)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

# S4 method for class '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
)
#> $g1
#> function (smean1, ...) 
#> smean1
#> <bytecode: 0x565150bf25f8>
#> <environment: 0x5651513e63d8>
#> 
#> $g2
#> function (smean1, smean2, n1, n2, ...) 
#> (n1 * smean1 + n2 * smean2)/(n1 + n2)
#> <bytecode: 0x565150bf2940>
#> <environment: 0x5651513e63d8>
#>