Analyze What Papers Actually Reported

Visual plan — Recreate Cox models from extracted data

Visualize input summaries first, then present the stepwise analytical plan and code/diagnostics to reproduce and test Cox proportional hazards models using the extracted summaries from both papers.

Stepwise analytical plan (reproducible)

1. Reconstruct time-to-event / risk sets: From the extracted summaries produce the minimal datasets required for Cox modeling. For the running paper: use exposure-hours and number of first-events (163 injuries) and group-level covariates (sex, experience, BMI categories) to reconstruct approximate person-time or use discrete-time Cox with grouped data. For the gag study: use individual-level records if available (authors used 3,954 records); if not, create a stratified aggregated survival table by condition category and available covariates (Gag survival Cox model (Fisheries Research)).
2. Specify Cox model formulae (examples):
   • Gag study: Surv(time, event) ~ condition_category + depth_interval + size + strata(release_year) + cluster(vessel) — include frailty/random effect for heterogeneity if needed.
   • Running study: Surv(time_hours, event) ~ sex + age + experience + BMI_cat + sport_type + strata(sex) or fit sex-specific models as authors did (Running injuries Cox models (BJSM)).
3. Implementation choices (recommended):
   • R: survival::coxph, cox.zph (proportionality), survminer for plots, coxme for mixed-effects/frailty terms.
   • Python: lifelines.CoxPHFitter with robust SEs and check_assumptions(); use strata and penalizers for small samples.
   • Use cluster-robust SEs if recaptures are correlated (e.g., vessel, angler) and include time-dependent covariates or interactions if PH violated.
4. Diagnostics and validation: check Schoenfeld residuals (cox.zph / coxph tests), plot scaled Schoenfeld residuals, inspect influential observations (dfbeta), test non-linearity (martingale residuals), compare AIC/likelihood, and assess model fit by concordance and calibration plots.
5. Sensitivity analyses: (a) sex-stratified models (running); (b) collapse condition categories differently; (c) include frailty/random-effects; (d) re-run with interval-censoring or grouped-time Cox if only summary exposure is available; (e) quantify how unmeasured predation or reporting bias in recaptures would change HR estimates (bias analysis).
6. Reporting and reproducibility: Provide the exact code (R/Python), random seeds, package versions, and a README describing how aggregated counts were reconstructed from the extracts. Note limitations: reconstructed datasets produce approximation error — mark as such and do not over-interpret causal claims (Gag study limitations; Running study limitations).

Minimal, reproducible code snippets (conceptual)

library(survival)
# example formula
fit <- coxph(Surv(time, event) ~ condition + size + strata(release_year) + cluster(vessel), data = gag_df)
summary(fit)
cox.zph(fit)  # proportional hazards test
# frailty example
fit_f <- coxph(Surv(time, event) ~ condition + frailty(vessel), data=gag_df)

from lifelines import CoxPHFitter
cph = CoxPHFitter()
cph.fit(df, duration_col='time', event_col='event', robust=True)
cph.print_summary()
cph.check_assumptions(df)

Key practical notes and limitations (concise)

• Reconstruction from aggregated summaries introduces approximation error — ideally obtain raw individual-level datasets (authors mention availability routes) before definitive inference (Gag data availability).
• Both original analyses flagged potential biases (self-report, short follow-up, unmeasured confounding) — sensitivity analyses and transparent uncertainty quantification are required (Running study limitations).

If you want full reproduction: click Run AI Scientist Analysis to (A) request raw datasets or reconstruct aggregate risk-sets algorithmically from the extracted summaries, (B) run R/Python Cox regressions with diagnostics and sensitivity analyses, (C) output reproducible code, tables, and publication-ready figures.