ccwr

ccwr is an R package for building reproducible clone-censor-weight (CCW) workflows for target trial emulation.

This repository is meant to be easy to use from a fresh GitHub clone and easy to maintain as a shared collaboration project. The recommended setup uses:

• rig to install and switch to the project R version
• renv to restore the same package environment for every collaborator
• GitHub Actions to check reproducibility and package health

Quick start

1. Clone the repository

git clone https://github.com/CausalInferenceLab/ccwr.git
cd ccwr

2. Use rig to install R 4.4.2

This project is pinned to R 4.4.2 for reproducibility.

rig add 4.4.2
rig default 4.4.2

If you already have R 4.4.2, you can skip rig add 4.4.2.

3. Restore the project package library with renv

Open R in the project directory and run:

install.packages("renv")
renv::restore()

renv::restore() installs the package versions recorded in renv.lock, so everyone works with the same dependency set.

Note: this repository includes a .Rprofile file that activates renv automatically when you open the project in R.

4. Install the package locally

From the project root:

R CMD INSTALL .

Then in R:

library(ccwr)

First example

library(ccwr)

data(lungcancer)

arms <- c("Control", "Surgery")
clones <- clone_arms(lungcancer, arms)

policies <- create_policy_A(
  arms,
  treatment = "surgery",
  time_to_treatment = "timetosurgery",
  grace_period = 182.62,
  outcome = "death",
  followup = "fup_obs",
  clone_outcome = "outcome",
  clone_followup = "fup"
)
clones_policy <- apply_logics(clones, policies)

censoring_logics <- create_censoring_logics_A(
  arms,
  treatment = "surgery",
  time_to_treatment = "timetosurgery",
  grace_period = 182.62,
  followup = "fup_obs",
  clone_censoring = "censoring",
  clone_uncensored_followup = "fup_uncensored"
)
clones_censored <- apply_logics(clones_policy, censoring_logics)

fup_uncensored is the strategy-specific follow-up time for the censoring rules. It remains the observed follow-up time when a clone adheres to its assigned strategy and is set to the artificial-censoring time only when that clone deviates from the strategy.

clones_final <- create_final_data(
  clones_censored,
  clone_followup = "fup",
  clone_outcome = "outcome",
  clone_censoring = "censoring",
  col_ids = "id"
)

clones_estimated <- estimate_censoring(
  clones_final,
  predictors = c("age", "sex"),
  method = "pooled_logit"
)
clones_weighted <- weight_cases(clones_estimated)

fit <- emul_estimate(
  clones_weighted,
  method = "Cox",
  weights = "weight_Cox",
  predictors = c("age", "sex")
)

exp(stats::coef(fit))

boot <- emul_estimate_bootstrap(
  lungcancer,
  arms = arms,
  id = "id",
  treatment = "surgery",
  time_to_treatment = "timetosurgery",
  grace_period = 182.62,
  outcome = "death",
  followup = "fup_obs",
  censoring_predictors = c("age", "sex"),
  predictors = c("age", "sex"),
  n_bootstrap = 200,
  seed = 1
)

The full process is:

1. Clone each patient into the target trial arms with clone_arms().
2. Define and apply treatment-policy logic with create_policy_A() and apply_logics().
3. Define and apply artificial censoring logic with create_censoring_logics_A() and apply_logics().
4. Expand cloned observations into long-form interval data with create_final_data().
5. Estimate censoring probabilities with estimate_censoring().
6. Add inverse probability of censoring weights with weight_cases().
7. Estimate the emulated trial effect with emul_estimate().
8. Use emul_estimate_bootstrap() to resample the original subjects and repeat the complete workflow when bootstrap confidence intervals are needed.

Pooled-logistic censoring models use a linear interval-start-time term by default. Set time_spline_df = 3 in estimate_censoring(), or censoring_time_spline_df = 3 in emul_estimate_bootstrap(), to use a natural cubic spline when the censoring data contain enough events to support the additional flexibility.

What the package currently provides

The package currently supports two-arm grace-period strategies for subject-level observational time-to-event data. It includes:

• read_trial_data() to read trial-style CSV data into a tibble
• make_surv_response() to build a survival::Surv() response object
• clone_arms() to duplicate observations across treatment strategies
• create_policy_A() and create_censoring_logics_A() to generate example treatment-policy and artificial-censoring logic for the lung cancer scenario
• apply_logics() to apply policy or censoring logic to cloned data
• create_final_data() to create long-form interval data for censoring models
• estimate_censoring() and weight_cases() to estimate censoring probabilities and add IPC weights
• emul_estimate() to estimate treatment effects
• emul_estimate_bootstrap() to obtain subject-level bootstrap confidence intervals by repeating cloning, censoring, weighting, and outcome estimation

Working together on this repository

For collaborative work, the safest pattern is:

1. Clone the repository.
2. Switch to R 4.4.2 with rig.
3. Run renv::restore().
4. Make your changes in a branch.
5. Run checks before opening a pull request.

Useful commands:

R CMD INSTALL .
R CMD check --no-manual .

In R:

testthat::test_local()

If you add, remove, or upgrade dependencies, update the lockfile from R:

renv::settings$snapshot.type("explicit")
renv::status(dev = TRUE)
renv::snapshot(dev = TRUE)

Please commit both code changes and the updated renv.lock when dependency changes are intentional.

Continuous integration

This repository includes two GitHub Actions workflows:

• check-reproducible.yaml runs R CMD check with pinned R 4.4.2 and renv
• check-latest.yaml runs broader checks across operating systems and R versions

Together, these workflows help keep the project reproducible for collaborators and stable for future users.

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