Depends: R (>= 3.0), RandomFieldsUtils (>= 0.5.4)
Packaged: 2021-09-23 15:48:15 UTC; schlather

Title: Adaptive Optimal Two-Stage Designs in R
person("Kevin", "Kunzmann", role = c("aut", "cre"), email = "kevin.kunzmann@mrc-bsu.cam.ac.uk"),
person("Maximilian", "Pilz", role = c("aut"), email = "pilz@imbi.uni-heidelberg.de")
Description: Optimize one or two-arm, two-stage designs for clinical trials with respect to several
pre-implemented objective criteria or implement custom objectives.
Optimization under uncertainty and conditional (given stage-one outcome) constraints are supported.
Suggests: knitr, rmarkdown, testthat, covr, rpact, vdiffr
Collate: adoptr.R util.R DataDistribution.R Prior.R PointMassPrior.R

Description: Optimize one or two-arm, two-stage designs for clinical trials with
respect to several pre-implemented objective criteria or implement custom
Optimization under uncertainty and conditional (given stage-one outcome)
See Pilz M, Kunzmann K, Herrmann C, Rauch G, Kieser M. A variational
approach to optimal two-stage designs. Statistics in Medicine. 2019;1–13.

approach to optimal two-stage designs. Statistics in Medicine. 2019;38(21):4159–4171.

Suggests: knitr, rmarkdown, testthat, covr, rpact, vdiffr, pwr, dplyr,

Collate: adoptr.R util.R DataDistribution.R BinomialDistribution.R
Author: Kevin Kunzmann [aut, cre] (<https://orcid.org/0000-0002-1140-7143>),

Title: Design and Analysis of Locally or Globally Efficient Adaptive
Authors@R: person("Kosuke", "Kashiwabara", email = "kashiwabara-tky@umin.ac.jp", role = c("aut", "cre"))
Description: Provides the functions for planning and conducting a
clinical trial with adaptive sample size determination. Maximal statistical
efficiency will be exploited even when dramatic or multiple adaptations
are made. Such a trial consists of adaptive determination of sample size
at an interim analysis and implementation of frequentist statistical test at the
interim and final analysis with a prefixed significance level. The required
assumptions for the stage-wise test statistics are independent and stationary
increments and normality. Predetermination of adaptation rule is not required.