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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

Analyzing Competing Risk Data Using the R timereg Package.

Thomas H Scheike¹, Mei-Jie Zhang

¹Department of Biostatistics, University of Copenhagen, Øster Farimagsgade 5 B, P.O.B. 2099, DK-1014 Copenhagen K, Denmark, URL: http://staff.pubhealth.ku.dk/~ts/

Journal of Statistical Software

|June 19, 2012

Summary

This summary is machine-generated.

Flexible competing risks regression models are introduced for analyzing cumulative incidence data. These models, available in R's timereg package, handle non-proportional hazards and provide confidence bands for predicted curves.

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Area of Science:

Biostatistics
Survival Analysis
Statistical Modeling

Background:

Competing risks are common in medical research, where multiple events can occur.
Traditional regression models may struggle with non-proportional hazards in competing risks settings.
Accurate analysis is crucial for understanding disease progression and treatment outcomes.

Purpose of the Study:

Introduce flexible competing risks regression models for analyzing transition probabilities.
Provide tools for goodness-of-fit testing of the proportionality assumption.
Demonstrate the application of these models to real-world medical data.

Main Methods:

Utilize the comp.risk() function from the R timereg package.
Specify regression models for cumulative incidence.
Implement goodness-of-fit tests for subdistribution hazards' proportionality.
Construct confidence bands for predicted cumulative incidence curves.

Main Results:

The flexible models accommodate non-proportional hazards, a common issue in competing risks data.
The Fine and Gray model is a special case within this framework.
The methods were successfully applied to follicular cell lymphoma data, revealing important non-proportionality.

Conclusions:

Flexible competing risks regression models offer a robust approach to analyzing complex survival data.
These models improve the analysis of datasets with non-proportional subdistribution hazards.
The R package provides practical tools for biostatisticians and researchers.