cce

cce is an R package for counterfactual comparator work in survival settings. It focuses on a practical workflow:

• validate normalized source tables
• build an analysis-ready cohort
• estimate adjusted VS-mode survival curves with g-formula, iptw_km, and iptw_cox
• project SOC-only curves under hazard-ratio scenarios
• export machine-readable results for downstream reporting

The package is designed for reproducibility-first development. It keeps inputs explicit, returns tidy outputs, and ships two end-to-end tutorials:

• a synthetic demo-data workflow
• a public oncology dataset workflow using survival::veteran

Documentation site: https://dai540.github.io/cce/

The pkgdown site publishes both tutorials as HTML articles and mirrors the function reference:

• Demo tutorial: https://dai540.github.io/cce/articles/demo-data-workflow.html
• Real-data tutorial: https://dai540.github.io/cce/articles/public-oncology-data.html

Installation

From source:

R CMD INSTALL cce

Quick start

library(cce)

demo <- cce_demo_data(n = 220, seed = 7)
analysis <- demo$analysis_data

vs_fit <- fit_cce_vs(
  data = analysis,
  arm = "arm",
  time = "time",
  event = "event",
  covariates = c("age", "sex", "stage_or_risk", "ps"),
  subgroup = "subgroup",
  tau = 365,
  landmark_times = c(180, 365),
  bootstrap = 10,
  seed = 11
)

head(as_effects_df(vs_fit))

soc_fit <- project_soc_only(
  data = analysis,
  arm = "arm",
  soc_level = "SOC",
  time = "time",
  event = "event",
  subgroup = "subgroup",
  tau = 365,
  hr_scenarios = c(0.65, 0.80, 1.00),
  target_delta_rmst = 30,
  prior_mean_log_hr = log(0.8),
  prior_sd_log_hr = 0.20,
  bootstrap = 10,
  seed = 99
)

head(as_effects_df(soc_fit))

Main functions

• cce_spec() creates a reusable schema contract
• validate_cce_tables() preflights normalized source tables and returns issues
• build_analysis_dataset() turns normalized tables into an analysis-ready set
• profile_cce_dataset() summarizes sample size, events, subgroup counts, and missingness
• fit_cce_vs() estimates g-formula, iptw_km, and iptw_cox comparator curves
• project_soc_only() runs assumption-based PH projections
• write_cce_results() writes results.json, curves.csv, effects.csv, and diagnostics.csv

Tutorials

• Demo-data workflow walks through bundled normalized tables, cohort assembly, VS-mode estimation, SOC-only projection, and file export.
• Public oncology data workflow shows the same analysis pattern on the real patient-level survival::veteran dataset.

Output contract

Both VS and SOC-only results include:

• tidy curve data
• effect summaries with RMST and landmark contrasts
• diagnostics
• machine-readable run metadata including covariates, thresholds, spec, exclusions, and dataset profile

SOC-only outputs are always labeled Projection (assumption-based).