Related Experiment Video
Updated: Aug 2, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Competing approaches to analysis of failure times with competing risks
T M Farley1, M M Ali, E Slaymaker
1Department of Reproductive Health and Research, World Health Organization, 20 Avenue Appia, 1211 Geneva 27, Switzerland. farleyt@who.int
Abstract:
For the analysis of time to event data in contraceptive studies when individuals are subject to competing causes for discontinuation, some authors have recently advocated the use of the cumulative incidence rate as a more appropriate measure to summarize data than the complement of the Kaplan-Meier estimate of discontinuation. The former method estimates the rate of discontinuation in the presence of competing causes, while the latter is a hypothetical rate that would be observed if discontinuations for the other reasons could not occur. The difference between the two methods of analysis is the continuous time equivalent of a debate that took place in the contraceptive literature in the 1960s, when several authors advocated the use of net (adjusted or single decrement life table rates) rates in preference to crude rates (multiple decrement life table rates). A small simulation study illustrates the interpretation of the two types of estimate - the complement of the Kaplan-Meier estimate corresponds to a hypothetical rate where discontinuations for other reasons did not occur, while the cumulative incidence gives systematically lower estimates. The Kaplan-Meier estimates are more appropriate when estimating the effectiveness of a contraceptive method, but the cumulative incidence estimates are more appropriate when making programmatic decisions regarding contraceptive methods. Other areas of application, such as cancer studies, may prefer to use the cumulative incidence estimates, but their use should be determined according to the application.
Related Concept Videos
Introduction To Survival Analysis
The primary goal of survival analysis is to estimate survival time—the time until a...
Actuarial Approach
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
Assumptions of Survival Analysis
Comparing the Survival Analysis of Two or More Groups
Hazard Rate
Parametric Survival Analysis: Weibull and Exponential Methods
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...

