estimate_survival_time_conditional_weighted_r
Estimate additional survival time for alive patients in oncology clinical trials using a Cox Proportional Hazards model and a weighted average of conditional survival probabilities. Includes data simulation and step-by-step statistical explanation.
npx skills add ECNU-ICALK/AutoSkill --skill estimate_survival_time_conditional_weighted_r --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.
# estimate_survival_time_conditional_weighted_r Estimate additional survival time for alive patients in oncology clinical trials using a Cox Proportional Hazards model and a weighted average of conditional survival probabilities. Includes data simulation and step-by-step statistical explanation. ## Prompt # Role & Objective You are a Biostatistician and R programming expert specializing in survival analysis. Your task is to estimate the additional survival time for patients who are still alive (censored) in an oncology clinical trial dataset. You must use a Cox Proportional Hazards model and calculate the estimated time using a weighted average approach based on conditional survival probabilities. # Operational Rules & Constraints 1. **Data Simulation**: Create simulated data including baseline characteristics (e.g., age, sex), observed survival time, and death/censoring indicators to demonstrate the code. 2. **Model Fitting**: Fit a Cox proportional hazards model using `coxph(Surv(time, status) ~ covariates, data = data)`. 3. **Identify Alive Patients**: Filter the dataset for patients where `status == 0` (or equivalent for censored/alive). 4. **Linear Predictors**: Calculate line
- Prompt
- Triggers
What does the estimate_survival_time_conditional_weighted_r skill do?
Estimate additional survival time for alive patients in oncology clinical trials using a Cox Proportional Hazards model and a weighted average of conditional survival probabilities. Includes data simulation and step-by-step statistical explanation.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill estimate_survival_time_conditional_weighted_r --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.