basket

Basket Trial Analysis

Implementation of multisource exchangeability models for Bayesian analyses of prespecified subgroups arising in the context of basket trial design and monitoring. The R 'basket' package facilitates implementation of the binary, symmetric multi-source exchangeability model (MEM) with posterior inference arising from both exact computation and Markov chain Monte Carlo sampling. Analysis output includes full posterior samples as well as posterior probabilities, highest posterior density (HPD) interval boundaries, effective sample sizes (ESS), mean and median estimations, posterior exchangeability probability matrices, and maximum a posteriori MEMs. In addition to providing "basketwise" analyses, the package includes similar calculations for "clusterwise" analyses for which subgroups are combined into meta-baskets, or clusters, using graphical clustering algorithms that treat the posterior exchangeability probabilities as edge weights. In addition plotting tools are provided to visualize basket and cluster densities as well as their exchangeability. References include Hyman, D.M., Puzanov, I., Subbiah, V., Faris, J.E., Chau, I., Blay, J.Y., Wolf, J., Raje, N.S., Diamond, E.L., Hollebecque, A. and Gervais, R (2015) <doi:10.1056/NEJMoa1502309>; Hobbs, B.P. and Landin, R. (2018) <doi:10.1002/sim.7893>; Hobbs, B.P., Kane, M.J., Hong, D.S. and Landin, R. (2018) <doi:10.1093/annonc/mdy457>; and Kaizer, A.M., Koopmeiners, J.S. and Hobbs, B.P. (2017) <doi:10.1093/biostatistics/kxx031>.

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Description file content

Package
basket
Title
Basket Trial Analysis
Version
0.9.2
Description
Implementation of multisource exchangeability models for Bayesian analyses of prespecified subgroups arising in the context of basket trial design and monitoring. The R 'basket' package facilitates implementation of the binary, symmetric multi-source exchangeability model (MEM) with posterior inference arising from both exact computation and Markov chain Monte Carlo sampling. Analysis output includes full posterior samples as well as posterior probabilities, highest posterior density (HPD) interval boundaries, effective sample sizes (ESS), mean and median estimations, posterior exchangeability probability matrices, and maximum a posteriori MEMs. In addition to providing "basketwise" analyses, the package includes similar calculations for "clusterwise" analyses for which subgroups are combined into meta-baskets, or clusters, using graphical clustering algorithms that treat the posterior exchangeability probabilities as edge weights. In addition plotting tools are provided to visualize basket and cluster densities as well as their exchangeability. References include Hyman, D.M., Puzanov, I., Subbiah, V., Faris, J.E., Chau, I., Blay, J.Y., Wolf, J., Raje, N.S., Diamond, E.L., Hollebecque, A. and Gervais, R (2015) ; Hobbs, B.P. and Landin, R. (2018) ; Hobbs, B.P., Kane, M.J., Hong, D.S. and Landin, R. (2018) ; and Kaizer, A.M., Koopmeiners, J.S. and Hobbs, B.P. (2017) .
Depends
R (>= 3.4.0)
License
LGPL-2
Maintainer
Michael J. Kane
Imports
GenSA, foreach, ggplot2, stats, tibble, tidyr, dplyr, igraph, gridExtra, itertools, crayon, cli
LazyData
true
Encoding
UTF-8
RoxygenNote
6.1.1
URL
BugReports
https://github.com/kaneplusplus/basket/issues
Suggests
knitr, rmarkdown, testthat, doParallel
VignetteBuilder
knitr
NeedsCompilation
no
Packaged
2019-05-11 23:28:08 UTC; mike
Author
Nan Chen [aut], Brian Hobbs [aut], Alex Kaizer [aut], Michael J. Kane [aut, cre] ()
Repository
CRAN
Date/Publication
2019-05-14 13:50:03 UTC

install.packages('basket')

0.9.2

3 months ago

https://github.com/kaneplusplus/basket

Michael J. Kane

LGPL-2

Depends on

R (>= 3.4.0)

Imports

GenSA, foreach, ggplot2, stats, tibble, tidyr, dplyr, igraph, gridExtra, itertools, crayon, cli

Suggests

knitr, rmarkdown, testthat, doParallel

Discussions