Gaussian Copula Regression Modeling for Marker Classification Metrics with Competing Risk Outcomes

Decisions regarding competing risks are usually based on a continuous-valued marker toward predicting a cause-specific outcome. The classification power of a marker can be summarized using the time-dependent receiver operating characteristic curve and the corresponding area under the curve (AUC). Th...

Full description

Saved in:
Bibliographic Details
Published in:International journal of mathematics and mathematical sciences 2024-01, Vol.2024, p.1-13
Main Authors: Vásquez, Alejandro Román, Escarela, Gabriel, Reyes-Cervantes, Hortensia Josefina, Núñez-Antonio, Gabriel
Format: Article
Language:English
Subjects:
Classification
Confidence intervals
Logit models
Normal distribution
Optimization algorithms
Prostate cancer
Random variables
Skewed distributions
Statistical analysis
Statistical models
Survival
Time dependence
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Decisions regarding competing risks are usually based on a continuous-valued marker toward predicting a cause-specific outcome. The classification power of a marker can be summarized using the time-dependent receiver operating characteristic curve and the corresponding area under the curve (AUC). This paper proposes a Gaussian copula-based model to represent the joint distribution of the continuous-valued marker, the overall survival time, and the cause-specific outcome. Then, it is used to characterize the time-varying ROC curve in the context of competing risks. Covariate effects are incorporated by linking linear components to the skewed normal distribution for the margin of the marker, a parametric proportional hazards model for the survival time, and a logit model for the cause of failure. Estimation is carried out using maximum likelihood, and a bootstrap technique is implemented to obtain confidence intervals for the AUC. Information-criteria strategies are employed to find a parsimonious model. The performance of the proposed model is evaluated in simulation studies, considering different sample sizes and censoring distributions. The methods are illustrated with the reanalysis of a prostate cancer clinical trial. The joint regression strategy produces a straightforward and flexible model of the time-dependent ROC curve in the presence of competing risks, enhancing the understanding of how covariates may affect the discrimination of a marker.
ISSN:0161-1712
1687-0425
DOI:10.1155/2024/1671254