Assumptions of Survival Analysis

Survival analysis, a statistical method, evaluates the time until an event occurs. It is commonly used in medicine to analyze life expectancy.

It is pivotal to select a clinically relevant event that is well-defined, clear, and observable for accurate analysis.

One crucial aspect is censoring, which occurs when data are incomplete due to events like death or a participant's exit from the study. For example, patients leaving a study have their data right-censored.

Independent censoring means that the reasons for censoring — like dropping out of a study — are unrelated to the outcome of interest.

Next, the Cox proportional hazards assumption assumes that the relative risk or hazard ratios between groups remain constant.

The stationarity assumption ensures that the probability of an event changing over time is the same for all study groups unless explicitly modeled otherwise.

Additionally, the follow-up length and sample size must be carefully determined to ensure sufficient event occurrences for robust analysis.