Linked from

The 21 pages that link to Survival analysis, each with the reason it gives.

• Conditional probabilityRelated: Hazard rates describe event risk conditional on survival up to a given time.

• Reliability engineeringRelated: Its methods handle lifetime data when some test items have not failed by the study's end.

• Statistical modelRelated: Its models handle event times and censoring that ordinary regression may not represent.

• Longitudinal studyRelated: It handles event timing and incomplete follow-up in many longitudinal studies.

• Time seriesRelated: Event times form temporal data with censoring that ordinary series methods do not address.

• Exponential distributionRelated: The exponential model is a basic survival distribution with constant hazard.

• PrognosisRelated: It estimates outcome timing when some patients have not yet experienced the event.

• MortalityRelated: It handles varying follow-up and censoring when estimating mortality over time.

• Mortality rateRelated: It estimates mortality over follow-up when participants enter or leave observation at different times.

• Actuarial scienceRelated: It supplies methods for modeling lifetimes and other event times.

• Predictive maintenanceRelated: It can estimate failure risk when many assets have not yet failed during observation.

• First-passage timeRelated: Survival probabilities track the process remaining short of its target before first arrival.

• Statistical graphicsRelated: Kaplan–Meier curves display estimated survival probabilities over time.

• Russian rouletteRelated: The game’s outcomes are stark examples of why survival data must include those who die.

• Statistical analysisRelated: It handles event times when some outcomes remain unobserved during study.