analyze: Analyze a dataset

In adestr: Estimation in Optimal Adaptive Two-Stage Designs

Analyze a dataset

Description

The analyze function can be used calculate the values of a list of point estimators, confidence intervals, and p-values for a given dataset.

Usage

analyze(
  data,
  statistics = list(),
  data_distribution,
  use_full_twoarm_sampling_distribution = FALSE,
  design,
  sigma,
  exact = FALSE
)

## S4 method for signature 'data.frame'
analyze(
  data,
  statistics = list(),
  data_distribution,
  use_full_twoarm_sampling_distribution = FALSE,
  design,
  sigma,
  exact = FALSE
)

Arguments

data	a data.frame containing the data to be analyzed.
statistics	a list of objects of class PointEstimator, ConfidenceInterval 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.

Details

Note that in adestr, statistics are codes as functions of the stage-wise sample means (and stage-wise sample variances if data_distribution is Student). In a first-step, the data is summarized to produce these parameters. Then, the list of statistics are evaluated at the values of these parameters.

The output of the analyze function also displays information on the hypothesis test and the interim decision. If the statistics list is empty, this will be the only information displayed.

Value

Results object containing the values of the statistics when applied to data.

Examples

set.seed(123)
dat <- data.frame(
  endpoint = c(rnorm(28, 0.3)),
  stage = rep(1, 28)
)
analyze(data = dat,
        statistics = list(),
        data_distribution = Normal(FALSE),
        design = get_example_design(),
        sigma = 1)

# The results suggest recruiting 32 patients for the second stage
dat <- rbind(
  dat,
  data.frame(
    endpoint = rnorm(32, mean = 0.3),
    stage = rep(2, 32)))
analyze(data = dat,
        statistics = get_example_statistics(),
        data_distribution = Normal(FALSE),
        design = get_example_design(),
        sigma = 1)

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