Package: CMFsurrogate 1.0
CMFsurrogate: Calibrated Model Fusion Approach to Combine Surrogate Markers
Uses a calibrated model fusion approach to optimally combine multiple surrogate markers. Specifically, two initial estimates of optimal composite scores of the markers are obtained; the optimal calibrated combination of the two estimated scores is then constructed which ensures both validity of the final combined score and optimality with respect to the proportion of treatment effect explained (PTE) by the final combined score. The primary function, pte.estimate.multiple(), estimates the PTE of the identified combination of multiple surrogate markers. Details are described in Wang et al (2022) <doi:10.1111/biom.13677>.
Authors:
CMFsurrogate_1.0.tar.gz
CMFsurrogate_1.0.zip(r-4.5)CMFsurrogate_1.0.zip(r-4.4)CMFsurrogate_1.0.zip(r-4.3)
CMFsurrogate_1.0.tgz(r-4.4-any)CMFsurrogate_1.0.tgz(r-4.3-any)
CMFsurrogate_1.0.tar.gz(r-4.5-noble)CMFsurrogate_1.0.tar.gz(r-4.4-noble)
CMFsurrogate_1.0.tgz(r-4.4-emscripten)CMFsurrogate_1.0.tgz(r-4.3-emscripten)
CMFsurrogate.pdf |CMFsurrogate.html✨
CMFsurrogate/json (API)
| # Install 'CMFsurrogate' in R: |
| install.packages('CMFsurrogate', repos = c('https://laylaparast.r-universe.dev', 'https://cloud.r-project.org')) |
- example.data - Example data
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 2 years agofrom:7b30dadfcc. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 16 2024 |
| R-4.5-win | OK | Nov 16 2024 |
| R-4.5-linux | OK | Nov 16 2024 |
| R-4.4-win | OK | Nov 16 2024 |
| R-4.4-mac | OK | Nov 16 2024 |
| R-4.3-win | OK | Nov 16 2024 |
| R-4.3-mac | OK | Nov 16 2024 |
Exports:gen.bootstrap.weightspte.estimate.multipleresam
Dependencies:MASS
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Example data | example.data |
| Generate bootstrap sample | gen.bootstrap.weights |
| Estimates the proportion of treatment effect explained by the optimal combination of multiple surrogate markers using a calibrated model fusion approach | pte.estimate.multiple |
| Estimates quantities using resampled data | resam |
