Version Compatibility

Reference examples tested with: lifelines 0.27+, scikit-survival 0.21+, statsmodels 0.14+, pandas 2.1+, numpy 1.26+. R packages cited (still the SOTA for survival): survival 3.8+, survRM2, cmprsk, riskRegression, mstate, flexsurv, icenReg, rpsftm.

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures
• R: packageVersion('<pkg>') then ?function_name

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Time-to-Event Analysis for Clinical Trials

"Analyze time-to-event endpoint" -> Estimate a hazard, survival probability, cumulative incidence, or restricted mean time using a method calibrated to (a) whether proportional hazards holds, (b) whether competing events exist, (c) whether censoring is informative, and (d) which estimand the trial targets under ICH E9(R1).

The Single Most Important Modern Insight -- PH Almost Never Holds

In modern oncology with checkpoint inhibitors, targeted therapies, crossover, and depleted high-risk subjects over follow-up, proportional hazards (PH) violations are the rule, not the exception. The Cross-Pharma NPH Working Group (Lin et al 2020 Stat Biopharm Res; Magirr-Burman 2021 Stat Biopharm Res 15(2):295) documented systematic PH violations across phase III oncology trials, particularly delayed-effect patterns from checkpoint inhibitors.

The Cox HR is a time-averaged log-hazard ratio under PH violation (Xu-O'Quigley 2000), which may or may not be the estimand of interest. RMST (Royston-Parmar 2013) provides a clinically interpretable, hazard-free alternative.