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Variable selection for mixture and promotion time cure rate models.

Abdullah Masud¹, Wanzhu Tu¹, Zhangsheng Yu²

¹1 Department of Biostatistics, Indiana University School of Medicine, Indianapolis, IN, USA.

Statistical Methods in Medical Research

|November 19, 2016

Summary

This summary is machine-generated.

This study introduces new variable selection methods for cure rate models, essential for analyzing clinical trial data with cured patients. These methods improve the analysis of failure-time data in medical research.

Keywords:

Bayesian information criterion Mixture cure rate model adaptive least absolute shrinkage and selection operators expectation-maximization algorithm promotion time cure rate model wheeze

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Evaluation of the Curing of Adhesive Systems by Rheological and Thermal Testing

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Published on: July 3, 2020

Area of Science:

Biostatistics
Clinical Trials
Survival Analysis

Background:

Failure-time data with cured patients are frequently encountered in clinical studies.
Cure rate models are standard for analyzing such data, but lack developed variable selection techniques.
Effective variable selection is crucial for accurate interpretation of cure rate models.

Purpose of the Study:

To propose novel variable selection methods for mixture and promotion time cure models.
To address the limitations in existing variable selection techniques for cure rate models.
To enhance the analysis of failure-time data in clinical research.

Main Methods:

Development of two least absolute shrinkage and selection operators (LASSO) based methods.
Application to both parametric and nonparametric baseline hazard functions within cure models.
Extensive simulation studies to evaluate method performance.

Main Results:

The proposed LASSO-based methods demonstrate effective variable selection for cure rate models.
Simulation results confirm the operating characteristics of the developed methods.
Successful illustration using real-world data from a childhood wheezing study.

Conclusions:

The new methods provide a robust approach to variable selection in cure rate models.
These advancements are expected to improve the analysis of clinical trial data with cured individuals.
The findings offer practical tools for biostatisticians and clinical researchers.