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Related Experiment Video

Updated: Jun 2, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

The Lindley distribution applied to competing risks lifetime data.

Josmar Mazucheli¹, Jorge A Achcar

¹Universidade Estadual de Maringá, DEs, PR, Brazil. jmazucheli@gmail.com

Computer Methods and Programs in Biomedicine

|May 10, 2011

Summary

This summary is machine-generated.

This study introduces a new competing risks distribution based on the Lindley distribution. It offers a simpler alternative for analyzing lifetime data with multiple failure causes.

More Related Videos

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Related Experiment Videos

Last Updated: Jun 2, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Area of Science:

Statistics
Survival Analysis
Reliability Engineering

Background:

Competing risks data is common in studies where outcomes can have multiple mutually exclusive causes.
Traditional analysis often uses Exponential or Weibull distributions.
There is a need for alternative distributions that simplify competing risks analysis.

Purpose of the Study:

To propose a simple competing risks distribution as an alternative to existing models.
To explore the use of the Lindley distribution in a competing risks framework.
To provide a new tool for lifetime data analysis.

Main Methods:

Developed a competing risks distribution assuming independent risks.
Utilized the Lindley distribution for each competing risk.
Assumed each subject experiences only one event type.

Main Results:

A novel competing risks distribution is formulated.
The proposed distribution offers a potentially simpler analytical approach.
Demonstrates the applicability of the Lindley distribution in this context.

Conclusions:

The proposed Lindley-based competing risks distribution is a viable alternative.
This model simplifies the analysis of lifetime data with multiple failure modes.
Further research can explore its performance against established models.