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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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Methodological Review of Classification Trees for Risk Stratification: An Application Example in the Obesity Paradox.

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Classification trees (CTs) enhance clinical risk stratification by uncovering complex patterns. While ensemble CTs like XGBoost offer high accuracy, simple CTs and interpretability methods like SHAP are crucial for personalized medicine.

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Area of Science:

  • Machine Learning in Clinical Research
  • Epidemiological Methods
  • Predictive Modeling

Background:

  • Classification trees (CTs) are machine learning algorithms increasingly used in clinical research for risk stratification.
  • Their interpretable decision rules are valuable for healthcare professionals.
  • This review details CT methodology and its application to the "obesity paradox" in critically ill patients.

Purpose of the Study:

  • To provide a rigorous overview of CT methodology for clinicians.
  • To illustrate CT utility in risk stratification using a case study.
  • To compare CT approaches with traditional logistic regression.

Main Methods:

  • Description of CT development, pruning, and validation.
  • Application of CART, CHAID, and XGBoost on ENPIC study data.
  • Comparison with logistic regression and use of SHAP values for interpretability.

Main Results:

  • CTs identified optimal cut-offs and non-linear predictor interactions.
  • A subgroup exhibiting the obesity paradox (reduced mortality) was identified.
  • XGBoost showed superior predictive performance but reduced interpretability.

Conclusions:

  • CTs are valuable for clinical epidemiology, revealing hidden patterns and improving risk stratification.
  • Ensemble models offer high accuracy, necessitating interpretability techniques like SHAP.
  • CTs support personalized medicine, requiring careful interpretation and validation.