1. Introduction
Survival analysis is a statistical method used to analyze time‑to‑event data, where the outcome is the time until a specific event occurs (e.g., death, relapse, recovery). Preparation for survival analysis involves careful planning, data collection, and methodological considerations to ensure valid and interpretable results.

2. Key Concepts
Event: The outcome of interest (e.g., death, disease recurrence).

Time: Duration from a defined starting point until the event occurs.

Censoring: When the event has not occurred during the observation period (e.g., patient lost to follow‑up).

Survival Function (S(t)): Probability of surviving beyond time t.

Hazard Function (h(t)): Instantaneous risk of experiencing the event at time t.

3. Steps in Preparation
A. Define Research Question
Clearly state the event of interest.

Example: “What is the median survival time for patients with advanced cancer under treatment X?”

B. Identify Cohort and Data Sources
Select appropriate study population.

Ensure reliable data sources (clinical trials, registries, electronic health records).

C. Determine Start and End Points
Start: Diagnosis, treatment initiation, or enrollment.

End: Event occurrence or censoring.

D. Handle Censoring
Distinguish between right‑censoring (event not observed) and left‑censoring (event occurred before observation).

Ensure proper coding in dataset.

E. Collect Covariates
Demographics, clinical variables, treatment details.

Important for multivariate survival models (e.g., Cox regression).

F. Data Cleaning
Check for missing values.

Verify time consistency (no negative survival times).

Standardize variable formats.

4. Statistical Tools
Kaplan‑Meier Estimator: Non‑parametric estimate of survival function.

Life Table: Groups survival times into intervals.

Log‑Rank Test: Compares survival curves between groups.

Cox Proportional Hazards Model: Assesses effect of covariates on hazard.

5. Software Preparation
R: Packages like survival, survminer.

SPSS: Survival analysis module.

Stata/SAS: Built‑in survival procedures.

6. Ethical Considerations
Protect patient confidentiality.

Ensure informed consent for data use.

Report results transparently.

7. Example
A study of 200 patients with heart failure tracks survival from diagnosis to death or censoring. Data preparation includes coding event status (1=death, 0=censored), recording time in months, and including covariates like age, sex, and treatment type.

8. Conclusion
Proper preparation for survival analysis ensures accurate, meaningful results. It requires clear definitions, careful data handling, and appropriate statistical tools.