Table of Contents

## What is competing risk in survival analysis?

Competing risks occur frequently in the analysis of survival data. A competing risk is an event whose occurrence precludes the occurrence of the primary event of interest. In a study examining time to death attributable to cardiovascular causes, death attributable to noncardiovascular causes is a competing risk.

## What is fine and gray model?

The Fine-Gray model more accurately assesses the risk of re-fracture when a competing risk is present. The estimations of cumulative incidence or rate of re-fracture were consistently higher by traditional survival analyses (Kaplan-Meier or Cox) compared with the Fine-Gray model.

## What data is required for survival analysis?

Introduction to Survival Data Survival analysis focuses on two important pieces of information: Whether or not a participant suffers the event of interest during the study period (i.e., a dichotomous or indicator variable often coded as 1=event occurred or 0=event did not occur during the study observation period.

## Is death a censoring event?

In practice, participant death is typically treated as a noninformative censoring event, and treatment and covariate effects are often estimated with the Cox proportional hazards method [7–10]. These cases were treated as censored cases in a previous analysis of the effect of the intervention [12].

## What is censoring in survival analysis?

Censoring. Censoring is a form of missing data problem in which time to event is not observed for reasons such as termination of study before all recruited subjects have shown the event of interest or the subject has left the study prior to experiencing an event. Censoring is common in survival analysis.

## How do you interpret hazard ratios in survival analysis?

Hazard is defined as the slope of the survival curve — a measure of how rapidly subjects are dying. The hazard ratio compares two treatments. If the hazard ratio is 2.0, then the rate of deaths in one treatment group is twice the rate in the other group.

## Is Regression a survival analysis?

Analogous to a linear regression analysis, a survival analysis typically examines the relationship of the survival variable (the time until the event) and the predictor variables (the covariates).

## Where is survival analysis used?

Survival Analysis is used to estimate the lifespan of a particular population under study. It is also called ‘Time to Event’ Analysis as the goal is to estimate the time for an individual or a group of individuals to experience an event of interest. This time estimate is the duration between birth and death events[1].

## What is left censoring in survival analysis?

Left-censoring occurs when we cannot observe the time when the event occurred. Some already knew (left-censored), some learned during a study (exact), some had not yet learned by end of study (right-censored).” Interval-censoring is also discussed in Survival Analysis: Introduction (Survival).