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The hazard rate, also known as the hazard function or failure rate, is a statistical measure used to describe the instantaneous rate at which an event occurs, given that the event has not yet happened. From a probabilistic perspective, it represents the likelihood that a subject will experience the event in a very small time interval, conditional on surviving up to the beginning of that interval. In terms of frequency, the hazard rate can be viewed as the ratio of the number of events to the...
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Computing the Hazard Ratios Associated With Explanatory Variables Using Machine Learning Models of Survival Data.

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Summary

Researchers developed a new method to calculate hazard ratios (HRs) from machine learning (ML) models for survival analysis. This approach, using SHapley Additive exPlanations, allows for better risk factor identification in complex datasets.

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Area of Science:

  • * Bioinformatics
  • * Computational Biology
  • * Machine Learning in Survival Analysis

Background:

  • * Cox proportional hazards (CoxPH) models are standard for survival data analysis and hazard ratio (HR) derivation.
  • * Nonlinear, tree-based machine learning (ML) models are increasingly used for survival analysis, but lack methods for HR computation.
  • * Existing methods do not provide HRs for explanatory variables from ML models, limiting their interpretability.

Purpose of the Study:

  • * To introduce a novel methodology for computing hazard ratios (HRs) from tree-based machine learning (ML) models.
  • * To enable the quantification of explanatory variables' contributions to survival predictions using ML.
  • * To bridge the gap between traditional CoxPH models and advanced ML techniques in survival analysis.

Main Methods:

  • * Utilized three public survival datasets (colon, breast, pan cancer) for comparative analysis.
  • * Employed XGBoost, a state-of-the-art ML model, and compared its performance with CoxPH.
  • * Developed a method to compute HRs from XGBoost by exponentiating SHapley Additive exPlanations (SHAP) values and calculating ratios of subgroup means.
  • * Used bootstrapping for 1,000 model iterations to compute confidence intervals (CIs).

Main Results:

  • * Comparable performance between CoxPH and XGBoost on colon and breast cancer datasets, with consistent HRs.
  • * Agreement in most variables for the pan-cancer dataset, but conflicting findings in two explanatory variables between CoxPH and XGBoost.
  • * Kaplan-Meier plots corroborated the findings derived from the XGBoost model in the pan-cancer data.

Conclusions:

  • * The developed method enables HR derivation from ML models, enhancing risk factor identification in complex survival data.
  • * This approach can improve the prediction accuracy of clinical trial outcomes by leveraging advanced ML techniques.
  • * Facilitates a more comprehensive understanding of prognostic factors in survival data analysis.