Whats on with multivariate cox regression?

tags: #supervisedml

• Applied where time-to-event analysis is important.
  • “Time to event” refers to the duration until a specified event of interest occurs in a study or observation.
  • This concept is central to survival analysis, which focuses on the time duration until an event, often in contexts related to health, engineering, or social sciences.
    • Survival Time: This metric quantifies the time from a defined starting point (e.g., diagnosis, treatment initiation) to the occurrence of the event.
  • Examples:
    • Clinical: Onset of a disease (e.g., time until a patient develops diabetes)
    • Behavioral: time until a customer churns or stops using a service
• Multivariate Cox regression, also known as Cox proportional hazards regression, is a statistical method used to analyze and interpret the relationship between the survival time of subjects and one or more predictor variables. It is used to assess the impact of several independent variables simultaneously on the time until an event occurs, such as death, disease recurrence, or failure of a system.
• Features:
  • Proportional Hazards Model: Foundation is proportional hazard assumption; which implies ratio of hazard rate for two individual is constant.