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Read data; validate that time column is non-negative numeric and event column is binary (0\u002F1).\n2. Compute Kaplan-Meier survival estimate for each group (or overall if no group column).\n3. Report median survival time and 95% confidence interval (Greenwood formula) per group.\n4. If multiple groups: run log-rank test (Mantel-Cox); report chi-square statistic and p-value.\n5. Generate Kaplan-Meier plot with confidence intervals, censoring tick marks, and at-risk table.\n6. If covariate columns provided:\n   a. Fit Cox proportional hazards model.\n   b. Test proportional hazards assumption (Schoenfeld residuals).\n   c. Report for each covariate: hazard ratio, 95% CI, z-statistic, p-value.\n   d. Generate forest plot of hazard ratios.\n7. Write results and plots (PDF) to output directory.\n8. Save the executable analysis script (`analysis_code.py` or `analysis_code.R`) and software version manifest (`session_info.txt`) used to produce results.\n\n## Output Contract\n\n- KM table (TSV): time, survival_probability, ci_lower, ci_upper, n_at_risk, n_events, group\n- Median survival table (TSV): group, median_survival, ci_lower, ci_upper\n- Log-rank test result (TSV): chi_square, df, p_value (if multiple groups)\n- Cox regression table (TSV): covariate, hazard_ratio, ci_lower, ci_upper, z_statistic, p_value (if covariates provided)\n- Kaplan-Meier plot with at-risk table (PDF)\n- Forest plot (PDF, Cox only)\n- Reproducibility artifacts: `analysis_code.py` or `analysis_code.R`, plus `session_info.txt`\n\n## Limits\n\n- Requires accurate censoring information; informative censoring (censored because of outcome-related reasons) biases results.\n- Cox model assumes proportional hazards — verify with Schoenfeld residuals test (p > 0.05 for each covariate).\n- Log-rank test has low power when hazard ratio changes over time (crossing survival curves).\n- Common failure: event column containing values other than 0\u002F1 (e.g., 2 for competing events) — binarize or use competing risks models.\n","# Skill: Survival Analysis\n\n## Use When\n\n- User wants to analyze time-to-event data (overall survival, disease-free survival, time to relapse)\n- User wants to compare survival curves between two or more groups (e.g., high vs. low expression, treated vs. untreated)\n- User wants to identify predictors of survival using multivariable Cox regression\n- Use cases: clinical trial outcomes, patient survival, cancer prognosis\n\n## Inputs\n\n- Required:\n  - Data table (CSV or TSV) with: time-to-event column (numeric), event indicator column (1 = event occurred, 0 = censored)\n  - Time column name\n  - Event column name\n- Optional:\n  - Group column name for Kaplan-Meier comparison and log-rank test (if omitted: single KM curve)\n  - Covariate column names for Cox regression (if provided: runs multivariable Cox model)\n  - Time unit label (default: `days`)\n  - Output directory (default: `.\u002Fsurvival_output`)\n\n## Workflow\n\n1. Read data; validate that time column is non-negative numeric and event column is binary (0\u002F1).\n2. Compute Kaplan-Meier survival estimate for each group (or overall if no group column).\n3. Report median survival time and 95% confidence interval (Greenwood formula) per group.\n4. If multiple groups: run log-rank test (Mantel-Cox); report chi-square statistic and p-value.\n5. Generate Kaplan-Meier plot with confidence intervals, censoring tick marks, and at-risk table.\n6. If covariate columns provided:\n   a. Fit Cox proportional hazards model.\n   b. Test proportional hazards assumption (Schoenfeld residuals).\n   c. Report for each covariate: hazard ratio, 95% CI, z-statistic, p-value.\n   d. Generate forest plot of hazard ratios.\n7. Write results and plots (PDF) to output directory.\n8. 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