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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("kkmann/adoptr")

Documentation

The documentation is hosted at https://kkmann.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/kkmann/adoptr-validation-report and the last build version is hosted at https://kkmann.github.io/adoptr-validation-report.

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Version

Install

install.packages('adoptr')

Monthly Downloads

355

Version

0.4.2

License

MIT + file LICENSE

Issues

2

Pull Requests

3

Stars

8

Forks

7

Maintainer

Kevin Kunzmann

Last Published

May 28th, 2021

Functions in adoptr (0.4.2)

posterior

Compute posterior distribution
Scores

Scores
ConditionalSampleSize-class

(Conditional) Sample Size of a Design
predictive_cdf

Predictive CDF
Student-class

Student's t data distribution
AverageN2-class

Regularization via L1 norm
make_tunable

Fix parameters during optimization
get_initial_design

Initial design
TwoStageDesign-class

Two-stage designs
subject_to

Create a collection of constraints
minimize

Find optimal two-stage design by constraint minimization
condition

Condition a prior on an interval
adoptr

Adaptive Optimal Two-Stage Designs
composite

Score Composition
tunable_parameters

Switch between numeric and S4 class representation of a design
get_lower_boundary_design

Boundary designs
c2

Query critical values of a design
Binomial-class

Binomial data distribution
bounds

Get support of a prior or data distribution
cumulative_distribution_function

Cumulative distribution function
probability_density_function

Probability density function
simulate,TwoStageDesign,numeric-method

Draw samples from a two-stage design
GroupSequentialDesign-class

Group-sequential two-stage designs
DataDistribution-class

Data distributions
Normal-class

Normal data distribution
n1

Query sample size of a design
OneStageDesign-class

One-stage designs
plot,TwoStageDesign-method

Plot TwoStageDesign with optional set of conditional scores
predictive_pdf

Predictive PDF
print.adoptrOptimizationResult

Printing an optimization result
MaximumSampleSize-class

Maximum Sample Size of a Design
Constraints

Formulating Constraints
ConditionalPower-class

(Conditional) Power of a Design
ContinuousPrior-class

Continuous univariate prior distributions
PointMassPrior-class

Univariate discrete point mass priors
Prior-class

Univariate prior on model parameter
N1-class

Regularize n1
expectation

Expected value of a function