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adoptr

Adaptive optimal two-stage designs for clinical trials with one or two arms. For details on the core theoretical background, see:

Pilz M, Kunzmann K, Herrmann C, Rauch G, Kieser M. A variational approach to

optimal two-stage designs. Statistics in Medicine. 2019;38(21):4159–4171. https://doi.org/10.1002/sim.8291

Installation

Install the latest CRAN release via

install.packages("adoptr")

and the development version directly from GitHub with:

devtools::install_github("optad/adoptr")

Documentation

The documentation is hosted at https://optad.github.io/adoptr.

Validation Report

We provide an extensive validation report for adoptr which is implemented using the bookdown package. The sources are available at https://github.com/optad/adoptr-validation-report and the last build version is hosted at https://optad.github.io/adoptr-validation-report.

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Version

Install

install.packages('adoptr')

Monthly Downloads

416

Version

1.1.1

License

MIT + file LICENSE

Issues

0

Pull Requests

0

Stars

1

Forks

1

Maintainer

Maximilian Pilz

Last Published

October 2nd, 2024

Functions in adoptr (1.1.1)

NestedModels-class

F-Distribution
TwoStageDesign-class

Two-stage designs
Pearson2xK-class

Pearson's chi-squared test for contingency tables
make_tunable

Fix parameters during optimization
TwoStageDesignSurvival-class

Two-stage design for time-to-event-endpoints
minimize

Find optimal two-stage design by constraint minimization
bounds

Get support of a prior or data distribution
get_lower_boundary_design

Boundary designs
simulate,TwoStageDesign,numeric-method

Draw samples from a two-stage design
probability_density_function

Probability density function
Survival-class

Log-rank test
expectation

Expected value of a function
tunable_parameters

Switch between numeric and S4 class representation of a design
SurvivalDesign

SurvivalDesign
subject_to

Create a collection of constraints
get_initial_design

Initial design
composite

Score Composition
print.adoptrOptimizationResult

Printing an optimization result
ZSquared-class

Distribution class of a squared normal distribution
condition

Condition a prior on an interval
adoptr

Adaptive Optimal Two-Stage Designs
predictive_pdf

Predictive PDF
plot,TwoStageDesign-method

Plot TwoStageDesign with optional set of conditional scores
n1

Query sample size of a design
c2

Query critical values of a design
Student-class

Student's t data distribution
Scores

Scores
cumulative_distribution_function

Cumulative distribution function
predictive_cdf

Predictive CDF
posterior

Compute posterior distribution
Constraints

Formulating Constraints
DataDistribution-class

Data distributions
ANOVA-class

Analysis of Variance
ConditionalPower-class

(Conditional) Power of a Design
ConditionalSampleSize-class

(Conditional) Sample Size of a Design
GroupSequentialDesign-class

Group-sequential two-stage designs
ContinuousPrior-class

Continuous univariate prior distributions
MaximumSampleSize-class

Maximum Sample Size of a Design
GroupSequentialDesignSurvival-class

Group-sequential two-stage designs for time-to-event-endpoints
Binomial-class

Binomial data distribution
N1-class

Regularize n1
AverageN2-class

Regularization via L1 norm
Normal-class

Normal data distribution
OneStageDesignSurvival-class

One-stage designs for time-to-event endpoints
ChiSquared-class

Chi-Squared data distribution
OneStageDesign-class

One-stage designs
Prior-class

Univariate prior on model parameter
PointMassPrior-class

Univariate discrete point mass priors