Survival-Analysis Planning

Scope

The bundled script validates a plan. It does not read time-to-event rows, fit models, draw curves, or provide an individual prognosis.

Start With the Estimand

Align:

1. Population — eligibility and analysis set.
2. Condition/comparison — intervention, exposure, or groups being contrasted.
3. Variable/endpoint — event definition and ascertainment.
4. Intercurrent-event strategy — how events such as discontinuation, rescue therapy, switching, or competing events relate to the question.
5. Population-level summary — risk, survival probability, restricted mean survival time, hazard contrast, quantile, or another justified measure.
6. Time horizon — clinically and statistically justified.

Record time zero, delayed entry, time scale, follow-up end, and calendar/data-cut date.

Endpoint Definition

Specify:

• exact event;
• competing events;
• recurrent events if relevant;
• ascertainment schedule and adjudication;
• censoring rules;
• handling of same-day and tied events;
• loss to follow-up;
• administrative censoring;
• endpoint changes and versioning.

Do not treat a competing event as ordinary independent censoring when the target is absolute event probability.

Descriptive Estimation

Kaplan-Meier estimates are suitable for survival from the event of interest under appropriate censoring assumptions. Report:

• numbers at risk;
• events and censoring;
• estimates at prespecified times with intervals;
• median only if estimable;
• follow-up distribution using an appropriate method;
• truncation where risk sets become uninformative.

When competing events exist, use cumulative-incidence methods for event probabilities. Naively censoring competing events in Kaplan-Meier can overestimate absolute incidence.

Group Comparisons

The log-rank test compares event-time distributions and is most powerful under proportional alternatives. It does not quantify an effect. Pre-specify alternatives if curves may cross or effects may be delayed.

A Cox model estimates a hazard contrast conditional on model specification. Before presenting a single hazard ratio:

• assess proportional hazards graphically and analytically;
• examine functional forms;
• evaluate influential observations and interactions;
• define adjustment variables a priori;
• account for clustering or stratification;
• avoid interpreting 1 − HR as a reduction in cumulative risk.

If proportional hazards is doubtful, consider:

• time-varying coefficients;
• piecewise effects;
• landmark effects;
• restricted mean survival time at a justified horizon;
• survival or cumulative-incidence differences at prespecified times;
• accelerated failure-time or flexible parametric models.

Report why the selected summary answers the research question.

Competing Risks

Distinguish:

• cause-specific hazard questions;
• cumulative-incidence/absolute-risk questions;
• subdistribution-hazard modeling.

State which question the method answers. A subdistribution hazard ratio is not directly a risk ratio. When modeling several event types, check that resulting probability estimates are coherent.

Primary reference: Austin, Lee, and Fine, competing risks.

Bias and Missingness

Plan for:

• informative censoring;
• delayed entry/left truncation;
• immortal time;
• time-dependent confounding;
• interval censoring;
• outcome misclassification;
• missing covariates;
• competing events;
• informative visit schedules;
• treatment switching and rescue treatment;
• site and calendar effects.

Specify sensitivity analyses tied to plausible departures from assumptions. A “best/worst case” alone is rarely sufficient.

Prediction Models

For time-to-event prediction:

• preserve the locked model and prediction horizon;
• evaluate calibration at prespecified times;
• report time-dependent discrimination with uncertainty;
• use appropriate handling of censoring;
• evaluate overall and subgroup performance;
• perform external validation in relevant settings;
• avoid selecting a horizon after viewing results.

Use TRIPOD+AI and PROBAST+AI.

Biomarker Evaluation

For a prognostic biomarker:

• analyze continuous form where scientifically justified;
• pre-specify transformations and threshold;
• avoid minimum-p-value cut-point searches;
• report assay and specimen handling;
• adjust for established prognostic factors;
• validate externally.

For a predictive biomarker, estimate and report a treatment-by-biomarker interaction in an appropriate design. Separate prognostic association from treatment-effect modification.

Uncertainty and Multiplicity

Include:

• confidence intervals for every primary effect;
• uncertainty in calibration/discrimination;
• prespecified alpha or interval interpretation;
• multiplicity strategy for outcomes, times, subgroups, and models;
• bootstrap or cross-validation details if used;
• sensitivity analyses;
• model optimism and overfitting assessment.

Do not turn a threshold-crossing p-value into clinical importance.

Primary Method Sources

• ICH E9(R1) estimands and sensitivity analysis
• Royston and Parmar, restricted mean survival time
• Austin and Fine, reporting competing-risk analyses

Plan Validator

python3 scripts/survival_plan_validator.py assets/survival_analysis_plan_template.json

An exit code of zero means required planning fields and selected consistency rules passed. It is not statistical approval.