aibot88 Survival Outcome — Diagnostics & Hypothesis Testing
Open-source skill.
Scope
Right-censored survival data only. This skill handles the standard scenario where observation ends before the event occurs (censored) or the event is observed (event). It does not handle left-censoring, interval-censoring, or competing risks.
Workflow
Read each step file in workflow/ before executing that step.
| Step | File | Executor | Output |
|---|---|---|---|
| Collect | workflow/01-collect-inputs.md |
Main Agent | Structured input summary |
| Diagnose | workflow/02-check-distribution.md |
Main Agent | PART 1 code block |
| Test | workflow/03-run-primary-test.md |
Main Agent | PART 2-3 code blocks |
Decision Tree
1. CHECK FOLLOW-UP & CENSORING
├── Censoring rate > 80% → Warning: limited events, wide CIs expected
├── Censoring rate < 5% → Note: standard regression may suffice
└── Otherwise → proceed normally
2. GROUP COMPARISON
├── 2 groups → Log-rank test + HR from univariate Cox
└── 3+ groups → Log-rank test + pairwise log-rank (Bonferroni)
Required Inputs
| Role | What to collect |
|---|---|
| Time variable | Continuous, ≥0, follow-up/survival time |
| Event indicator | Which value = event occurred, which = censored |
| Group variable | What defines groups, how many levels |
| Predictors | For recommendation block (not executed) |
Code Structure
PART 0: Setup & Data Loading
PART 1: Follow-Up & Censoring Diagnostics → plot_01_km_overall.png, plot_01b_event_histogram.png
PART 2: Primary Hypothesis Test → plot_02_km_groups.png
PART 3: Recommendation Block → text listing additional analyses available
Reporting Standards
- p-values: "< .001" not "0.000"; exact to 3 decimals otherwise
- Hazard ratio: HR with 95% CI, "HR = X.XX, 95% CI [X.XX, X.XX]"
- Median survival: always with 95% CI
- Survival rates at landmarks: with 95% CI
- Censoring: always report % censored overall and by group
- Log-rank: chi-sq(df) = X.XX, p = .XXX
- Decimal places: 2 for median survival, 3 for p and HR
- Non-significance: "not statistically significant at alpha = .05" — never "no effect"
Hypothesis Tests
| Scenario | Test |
|---|---|
| 2 independent groups | Log-rank + univariate Cox HR |
| 3+ independent groups | Log-rank + pairwise log-rank (Bonferroni) |
Paired/clustered designs are out of scope for this version.
Example Dataset
R built-in survival::lung: outcome = time (survival time), status (1=censored, 2=dead).
Predictors: age, sex, ph.ecog, ph.karno, pat.karno, meal.cal, wt.loss.
Python: from lifelines.datasets import load_lung or reconstruct from R.
Cross-Skill Interface
Output:
├── code_r → .R script
├── code_python → .py script
├── figures/ → 2 PNGs (KM overall + KM groups)
└── recommendations → text block (additional analyses available)