goldilocks
Implement Goldilocks Bayesian adaptive design for time-to-event outcomes using a piecewise exponential distribution.
Science Score: 13.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
○CITATION.cff file
-
✓codemeta.json file
Found codemeta.json file -
○.zenodo.json file
-
○DOI references
-
○Academic publication links
-
○Committers with academic emails
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (19.4%) to scientific vocabulary
Keywords
adaptive
bayesian
bayesian-statistics
clinical-trials
rpackage
rstats
statistics
Last synced: 6 months ago
·
JSON representation
Repository
Implement Goldilocks Bayesian adaptive design for time-to-event outcomes using a piecewise exponential distribution.
Basic Info
Statistics
- Stars: 7
- Watchers: 1
- Forks: 2
- Open Issues: 4
- Releases: 1
Topics
adaptive
bayesian
bayesian-statistics
clinical-trials
rpackage
rstats
statistics
Created over 5 years ago
· Last pushed about 1 year ago
Metadata Files
Readme
Changelog
License
README.Rmd
---
output: github_document
editor_options:
markdown:
wrap: 72
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# goldilocks 
[](https://CRAN.R-project.org/package=goldilocks)
[](https://CRAN.R-project.org/package=goldilocks)
[](https://app.codecov.io/gh/graemeleehickey/goldilocks)
[](https://github.com/graemeleehickey/goldilocks/actions/workflows/R-CMD-check.yaml)
The goal of `goldilocks` is to implement the Goldilocks Bayesian
adaptive design proposed by Broglio et al. (2014) for time-to-event
endpoint trials, both one- and two-arm, with an underlying piecewise
exponential hazard model.
The method can be used for a confirmatory trial to select a trial's
sample size based on accumulating data. During accrual, frequent sample
size selection analyses are made and predictive probabilities are used
to determine whether the current sample size is sufficient or whether
continuing accrual would be futile. The algorithm explicitly accounts
for complete follow-up of all patients before the primary analysis is
conducted. Final analysis tests include the log-rank test, Cox
proportional hazards regression Wald test, and a Bayesian test that
compares the absolute difference in cumulative incidence functions at a
fixed time point.
Broglio et al. (2014) refer to this as a *Goldilocks trial design*, as
it is constantly asking the question, "Is the sample size too big, too
small, or just right?"
## Key benefits
Other software and R packages are available to implement this algorithm.
However, when designing studies it is generally required that many
thousands of trials are simulated to adequately characterize the
operating characteristics, e.g. type I error and power. Hence, a
computationally efficient and fast algorithm is helpful. The
`goldilocks` package takes advantage of many tools to achieve this:
- Log-rank tests are implemented via code from the
[`fastlogranktest`](https://CRAN.R-project.org/package=fastlogranktest)
package, which uses a lightweight C++ implementation
- Piecewise exponential simulation is implemented via the
[`PWEALL`](https://CRAN.R-project.org/package=PWEALL) package, which
uses a lightweight Fortran implementation
- Simulation of multiple trials can be performed in parallel using the
[`pbmcapply`](https://CRAN.R-project.org/package=pbmcapply) package
**Note**: because `fastlogranktest` is no longer available on CRAN, a
copy of the C++ code and wrapper have been incorporated directly into
this package.
## References
Broglio KR, Connor JT, Berry SM. Not too big, not too small: a
Goldilocks approach to sample size selection. *Journal of
Biopharmaceutical Statistics*, 2014; **24(3)**: 685–705.
## Installation
You can install the development version of `goldilocks`
[GitHub](https://github.com/) with:
```{r eval=FALSE}
# install.packages("devtools")
devtools::install_github("graemeleehickey/goldilocks")
```
Owner
- Name: Graeme Hickey
- Login: graemeleehickey
- Kind: user
- Location: Liverpool
- Company: Medtronic
- Website: www.glhickey.com
- Twitter: graemeleehickey
- Repositories: 27
- Profile: https://github.com/graemeleehickey
Senior Director of Statistics | Medtronic Structural Heart & Aortic
GitHub Events
Total
- Release event: 1
- Watch event: 1
- Push event: 3
- Fork event: 1
- Create event: 1
Last Year
- Release event: 1
- Watch event: 1
- Push event: 3
- Fork event: 1
- Create event: 1
Committers
Last synced: about 2 years ago
Top Committers
| Name | Commits | |
|---|---|---|
| Graeme Hickey | g****y@g****m | 94 |
| Graeme Hickey | g****y@b****m | 21 |
Committer Domains (Top 20 + Academic)
bd.com: 1
Issues and Pull Requests
Last synced: 7 months ago
All Time
- Total issues: 22
- Total pull requests: 0
- Average time to close issues: 20 days
- Average time to close pull requests: N/A
- Total issue authors: 2
- Total pull request authors: 0
- Average comments per issue: 0.18
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- graemeleehickey (21)
- bd-graeme-hickey (1)
Pull Request Authors
Top Labels
Issue Labels
enhancement (4)
Pull Request Labels
Packages
- Total packages: 1
-
Total downloads:
- cran 199 last-month
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 2
- Total maintainers: 1
cran.r-project.org: goldilocks
Goldilocks Adaptive Trial Designs for Time-to-Event Endpoints
- Homepage: https://github.com/graemeleehickey/goldilocks
- Documentation: http://cran.r-project.org/web/packages/goldilocks/goldilocks.pdf
- License: GPL-3
-
Latest release: 0.4.0
published about 1 year ago
Rankings
Dependent packages count: 29.8%
Dependent repos count: 35.5%
Average: 46.1%
Downloads: 73.0%
Maintainers (1)
Last synced:
6 months ago