On the integration of decision trees with mixture cure model
Wisdom Aselisewine1, Suvra Pal1
1Department of Mathematics, University of Texas at Arlington, Arlington, Texas, USA.
Statistics in Medicine
|July 28, 2023
Summary
This study introduces a new mixture cure model using decision trees for better survival data analysis. The proposed model improves prediction accuracy and interpretability compared to standard methods.
Area of Science:
- Biostatistics
- Machine Learning
- Survival Analysis
Background:
- Mixture cure models analyze survival data with a cured subgroup.
- Logistic regression for incidence may lack predictive accuracy with non-linear effects.
- Machine learning offers better classification but often sacrifices interpretability.
Purpose of the Study:
- To propose a novel mixture cure model balancing predictive accuracy and interpretability.
- To model the incidence part using a decision tree classifier.
- To preserve the proportional hazards structure for the latency part.
Main Methods:
- Developed a mixture cure model incorporating a decision tree classifier for incidence.
- Preserved the proportional hazards structure for the latency component.
- Utilized an expectation-maximization algorithm for parameter estimation.
Main Results:
- The proposed decision tree-based mixture cure model demonstrated superior performance.
- Outperformed logistic regression- and spline regression-based models in fitting and prediction.
- Showcased improved handling of linear and non-linear covariate effects.
Conclusions:
- The new model offers enhanced interpretability and predictive accuracy for survival data.
- It provides a flexible approach to modeling both linear and non-linear covariate effects.
- The decision tree approach effectively addresses limitations of traditional methods in mixture cure modeling.
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