Introduction | Target trial emulation | The problem it solves | Data | Reading your own data | Clone-censor-weighting | Clone | Censor | The intuition, traced by hand | Deriving the grace-period rules | Expanding to person-time | Weight | Estimating the effect | Summary | References
Package: ccwr 0.0.1
Sang Ho Park
ccwr: Clone-Censor-Weight Methods for Target Trial Emulation
Provides methods for clone-censor-weight analyses that emulate target trials with grace-period treatment strategies using observational time-to-event data. Tools clone participants across strategies, apply strategy-specific artificial censoring, and estimate inverse probability of censoring weights using pooled logistic or Cox models, fit weighted outcome models, and obtain subject-level bootstrap confidence intervals by repeating the complete analysis. The methods are described by Maringe et al. (2020) <doi:10.1093/ije/dyaa057> and Gaber et al. (2024) <doi:10.1002/cam4.70461>.
Authors:
ccwr_0.0.1.tar.gz
ccwr_0.0.1.zip(r-4.7-any)ccwr_0.0.1.zip(r-4.6-any)ccwr_0.0.1.zip(r-4.5-any)
ccwr_0.0.1.tgz(r-4.6-any)ccwr_0.0.1.tgz(r-4.5-any)
ccwr_0.0.1.tar.gz(r-4.7-any)ccwr_0.0.1.tar.gz(r-4.6-any)
ccwr_0.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
ccwr/json (API)
| # Install 'ccwr' in R: |
| install.packages('ccwr', repos = c('https://causalinferencelab.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/causalinferencelab/ccwr/issues
Datasets:
- lungcancer - Simulated lung cancer patients data by Maringe et al.
- patients13 - 13 types of patient records in Maringe et al.
Last updated from:d27a4e8a48. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 124 | ||
| source / vignettes | OK | 156 | ||
| linux-release | OK | 148 | ||
| macos-release | OK | 194 | ||
| macos-oldrel | OK | 122 | ||
| windows-devel | OK | 93 | ||
| windows-release | OK | 88 | ||
| windows-oldrel | OK | 76 | ||
| wasm-release | OK | 106 |
Exports:apply_logicsclone_armscreate_censoring_logics_Acreate_final_datacreate_policy_Aemul_estimateemul_estimate_bootstrapestimate_censoringmake_surv_responseread_trial_dataweight_cases
Dependencies:bitbit64clicliprcpp11crayondplyrgenericsgluehmslatticelifecyclemagrittrMatrixpillarpkgconfigprettyunitsprogressR6readrrlangsurvivaltibbletidyselecttzdbutf8vctrsvroomwithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Apply case_when logics (e.g. policy, censoring) to clones | apply_logics |
| Duplicate data frame for each treatment arm to emulate | clone_arms |
| Generate censoring logic for scenario A | create_censoring_logics_A |
| Create training data for censoring probability estimation | create_final_data |
| Generate clone policy for scenario A | create_policy_A |
| Estimate the emulated trial effect | emul_estimate |
| Bootstrap a complete clone-censor-weight analysis | emul_estimate_bootstrap |
| Estimate censoring probabilities | estimate_censoring |
| Simulated lung cancer patients data by Maringe et al. (2020) | lungcancer |
| Construct a survival response | make_surv_response |
| 13 types of patient records in Maringe et al. (2020) | patients13 |
| Read trial-style data from CSV | read_trial_data |
| Add inverse probability of censoring weights | weight_cases |