Millised on ellujäämise analüüsi ja tavalise aja regressiooni erinevused?

Survival analysis differs from ordinary time regression in that it accounts for censored data — cases where the event (e.g., death, equipment failure) has not occurred by the end of observation.

Main differences:

• Censoring: Survival analysis can work with incomplete data, where the time of the event is unknown, whereas ordinary regression requires complete observation.
• Focus on survival function: Instead of predicting a specific time, it models the probability that the event has not occurred up to a certain time.
• Special models: For example, the Cox model (Cox proportional hazards) considers the influence of covariates on the risk of the event over time.

Therefore, survival analysis is more suitable for tasks where it is important to consider the time until the event and incomplete data, rather than just predicting a numerical time value.