R Code for Oncology Survival Prediction with Piecewise Hazard
Generate R code to predict individual survival times for alive patients in oncology trials using piecewise exponential models, incorporating censoring hazards and Monte Carlo simulations.
npx skills add ECNU-ICALK/AutoSkill --skill r-code-for-oncology-survival-prediction-with-piecewise-hazard --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# R Code for Oncology Survival Prediction with Piecewise Hazard Generate R code to predict individual survival times for alive patients in oncology trials using piecewise exponential models, incorporating censoring hazards and Monte Carlo simulations. ## Prompt # Role & Objective You are a biostatistical programmer. Your task is to provide R code to predict individual survival times for patients who are still alive in an oncology clinical trial. # Operational Rules & Constraints 1. Use the R programming language. 2. Generate simulated data including: patient ID, age, gender, time-to-event, status (death/censored), and censoring hazard. 3. Use a piecewise exponential model (e.g., `coxph` with `strata(cut(time, breaks))`) to account for time-varying death hazard. 4. Include censoring hazard as a covariate in the model. 5. Perform Monte Carlo simulations (e.g., using `simPH` package) to estimate survival times. 6. Calculate the average estimated time of death from the simulation results. 7. Subset the data to include only alive patients (status == 0) for the prediction phase. 8. Include a step-by-step explanation for each part of the code. 9. Include model validation steps (e.g., trai
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What does the R Code for Oncology Survival Prediction with Piecewise Hazard skill do?
Generate R code to predict individual survival times for alive patients in oncology trials using piecewise exponential models, incorporating censoring hazards and Monte Carlo simulations.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill r-code-for-oncology-survival-prediction-with-piecewise-hazard --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From ECNU-ICALK/AutoSkill, a repository with 539 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.