Vertical modeling: analysis of competing risks data with a cure fraction

In this paper, we extend the vertical modeling approach for the analysis of survival data with competing risks to incorporate a cure fraction in the population, that is, a proportion of the population for which none of the competing events can occur. The proposed method has three components: the pro...

Full description

Saved in:
Bibliographic Details
Published in:Lifetime data analysis 2019-01, Vol.25 (1), p.1-25
Main Authors: Nicolaie, Mioara Alina, Taylor, Jeremy M. G., Legrand, Catherine
Format: Article
Language:English
Subjects:
Algorithms
Cancer
Computer Simulation
Data Accuracy
Data Analysis
Economics
Failure analysis
Finance
Health Sciences
Humans
Insurance
Kaplan-Meier Estimate
Likelihood Functions
Management
Mathematics and Statistics
Medicine
Modelling
Models, Statistical
Operations Research/Decision Theory
Parameter estimation
Prepayments
Quality Control
Regression analysis
Reliability
Risk Assessment
Safety and Risk
Statistics
Statistics for Business
Statistics for Life Sciences
Survival
Survival Analysis
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this paper, we extend the vertical modeling approach for the analysis of survival data with competing risks to incorporate a cure fraction in the population, that is, a proportion of the population for which none of the competing events can occur. The proposed method has three components: the proportion of cure, the risk of failure, irrespective of the cause, and the relative risk of a certain cause of failure, given a failure occurred. Covariates may affect each of these components. An appealing aspect of the method is that it is a natural extension to competing risks of the semi-parametric mixture cure model in ordinary survival analysis; thus, causes of failure are assigned only if a failure occurs. This contrasts with the existing mixture cure model for competing risks of Larson and Dinse, which conditions at the onset on the future status presumably attained. Regression parameter estimates are obtained using an EM-algorithm. The performance of the estimators is evaluated in a simulation study. The method is illustrated using a melanoma cancer data set.
ISSN:1380-7870
1572-9249
DOI:10.1007/s10985-018-9417-8