What this package does | Example data | Design a hierarchy | Censoring in time-to-event levels | Score the pairs | Unadjusted win statistics | Fit the model | Interpreting delta | Standard errors | Prediction and calibration | Large cohorts | Threads | Citation | Where to go next
Package: hpsreg 1.0.0
hpsreg: Regression for Hierarchically Ordered Clinical Outcomes via Pairwise Comparisons (Bradley-Terry-Davidson Model)
Provides regression for hierarchically ordered clinical outcomes via pairwise comparisons, using a covariate-adjusted Davidson (Bradley-Terry with ties) model with U-statistic sandwich standard errors. A hierarchy of outcomes (binary, ordinal, continuous, and time-to-event, with eligibility guards and tie margins) is defined; all pairs of subjects are scored lexicographically; and the resulting win / loss / tie data are analysed. Unadjusted win statistics (net benefit, win ratio, win odds), diagnostics, and a worked example based on outcomes after allogeneic stem cell transplant are also provided. The computational engine is written in 'C++' using 'Rcpp' and 'RcppEigen'.
Authors:
hpsreg_1.0.0.tar.gz
hpsreg_1.0.0.zip(r-4.7-x86_64)hpsreg_1.0.0.zip(r-4.7-arm64)hpsreg_1.0.0.zip(r-4.6-x86_64)hpsreg_1.0.0.zip(r-4.6-arm64)hpsreg_1.0.0.zip(r-4.5-x86_64)
hpsreg_1.0.0.tgz(r-4.6-x86_64)hpsreg_1.0.0.tgz(r-4.6-arm64)hpsreg_1.0.0.tgz(r-4.5-x86_64)hpsreg_1.0.0.tgz(r-4.5-arm64)
hpsreg_1.0.0.tar.gz(r-4.7-arm64)hpsreg_1.0.0.tar.gz(r-4.7-x86_64)hpsreg_1.0.0.tar.gz(r-4.6-arm64)hpsreg_1.0.0.tar.gz(r-4.6-x86_64)
hpsreg_1.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
hpsreg/json (API)
| # Install 'hpsreg' in R: |
| install.packages('hpsreg', repos = c('https://rvaradhan.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:5d1a6e325d. Checks:15 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 134 | ||
| linux-devel-x86_64 | OK | 149 | ||
| source / vignettes | OK | 391 | ||
| linux-release-arm64 | OK | 151 | ||
| linux-release-x86_64 | OK | 201 | ||
| macos-release-arm64 | OK | 152 | ||
| macos-release-x86_64 | OK | 196 | ||
| macos-oldrel-arm64 | OK | 121 | ||
| macos-oldrel-x86_64 | OK | 278 | ||
| windows-devel-arm64 | OK | 228 | ||
| windows-devel-x86_64 | OK | 168 | ||
| windows-release-arm64 | OK | 198 | ||
| windows-release-x86_64 | OK | 187 | ||
| windows-oldrel-x86_64 | OK | 124 | ||
| wasm-release | OK | 138 |
Exports:btdbtd_calibrationbtd_controlbtd_pair_probshierarchyhps_betahps_cox_grfshps_hierarchylvl_binarylvl_continuouslvl_customlvl_grouplvl_ordinallvl_tteopenmp_threadsresolutionsample_pairsscore_pairssimulate_hpswin_stats
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Covariate-adjusted analysis of hierarchical pairwise outcomes | btd summary.btd |
| Calibration of the pairwise model | btd_calibration |
| Control parameters for the Bradley-Terry-Davidson fit | btd_control |
| Outcome levels for a hierarchy | hb_levels lvl_binary lvl_continuous lvl_custom lvl_ordinal lvl_tte |
| Define a hierarchy of outcomes | hierarchy |
| Data-generating coefficients for the transplant example | hps_beta |
| Composite m-GRFS Cox model (comparator for the example) | hps_cox_grfs |
| Hierarchy for the stem cell transplant example | hps_hierarchy |
| Group several levels into one reporting level | lvl_group |
| OpenMP availability | openmp_threads |
| Plot a calibration table | plot.btd_calibration |
| Predicted latent score and pair probabilities | btd_pair_probs predict.btd |
| Where are pairs decided? | resolution |
| Sample a random set of pairs | sample_pairs |
| Score all pairs of subjects under a hierarchy | score_pairs |
| Simulate a transplant cohort | simulate_hps |
| Win statistics for two groups | win_stats |