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First, times to event are always positive and their distributions are often skewed. There are unique features of time to event variables.

Interpret coefficients in Cox proportional hazards regression analysis.

Perform and interpret the log rank test.

Construct a life table using the Kaplan-Meier approach.

Construct a life table using the actuarial approach.

Identify applications with time to event outcomes.

The questions of interest in survival analysis are questions like: What is the probability that a participant survives 5 years? Are there differences in survival between groups (e.g., between those assigned to a new versus a standard drug in a clinical trial)? How do certain personal, behavioral or clinical characteristics affect participants' chances of survival? Learning ObjectivesĪfter completing this module, the student will be able to: What we mean by 'survival' in this context is remaining free of a particular outcome over time. Statistical analysis of these variables is called time to event analysis or survival analysis even though the outcome is not always death. Statistical analysis of time to event variables requires different techniques than those described thus far for other types of outcomes because of the unique features of time to event variables.

A time to event variable reflects the time until a participant has an event of interest (e.g., heart attack, goes into cancer remission, death). This module introduces statistical techniques to analyze a ' time to event outcome variable,' which is a different type of outcome variable than those considered in the previous modules. Boston University School of Public Health

Blood pressure chart in excel date and time