Step 1: Load R package 'wcox' and toy data. | Step 2: Acquire population incidence rate in the correct format. | Step 3: Prepare the sample data using Prepare_data(). | Step 4: Calculate weights using Calculate_weights(). | Step 5: Fit a weighted Cox model using R package 'survival'. | References
Package: wcox 1.0
wcox: Weights to Correct for Outcome Dependent Sampling in Time to Event Data
A new inverse probability of selection weighted Cox model to deal with outcome-dependent sampling in survival analysis.
Authors:
wcox_1.0.tar.gz
wcox_1.0.zip(r-4.7-any)wcox_1.0.zip(r-4.6-any)wcox_1.0.zip(r-4.5-any)
wcox_1.0.tgz(r-4.6-any)wcox_1.0.tgz(r-4.5-any)
wcox_1.0.tar.gz(r-4.7-any)wcox_1.0.tar.gz(r-4.6-any)
wcox_1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
wcox/json (API)
| # Install 'wcox' in R: |
| install.packages('wcox', repos = c('https://vharntzen.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/vharntzen/wcox/issues
Datasets:
- fam_dat - Simulated time-to-event data.
Last updated from:9f42d5d8a9. Checks:7 NOTE, 2 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | NOTE | 122 | ||
| source / vignettes | OK | 165 | ||
| linux-release | NOTE | 165 | ||
| macos-release | NOTE | 136 | ||
| macos-oldrel | NOTE | 179 | ||
| windows-devel | NOTE | 76 | ||
| windows-release | NOTE | 75 | ||
| windows-oldrel | NOTE | 73 | ||
| wasm-release | OK | 97 |
Exports:Calculate_weightsPrepare_data
Dependencies:clicpp11dplyrgenericsgluelatticelifecyclemagrittrMatrixpillarpkgconfigpurrrR6rlangstringistringrsurvivaltibbletidyrtidyselectutf8vctrswithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Calculate inverse probability of selection weights. | Calculate_weights |
| Simulated time-to-event data. | fam_dat |
| Prepare data for weight calculation using the population incidence. | Prepare_data |