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Related Concept Videos

Hazard Ratio01:12

Hazard Ratio

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The hazard ratio (HR) is a widely used measure in clinical trials to compare the risk of events, such as death or disease recurrence, between two groups over time. It reflects the ratio of hazard rates—the instantaneous risk of the event occurring—between a treatment group and a control group. This measure provides valuable insights into the relative effectiveness of a treatment by assessing how the risk of an event differs between the two groups.
For example, in a clinical trial...
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Hazard Rate01:11

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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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Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
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Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
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Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
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BoXHED: Boosted eXact Hazard Estimator with Dynamic covariates.

Xiaochen Wang1, Arash Pakbin2, Bobak J Mortazavi2

  • 1Biostatistics Department, Yale University, New Haven, Connecticut, USA.

Proceedings of Machine Learning Research
|February 22, 2021
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Summary

This study introduces BoXHED, a new software for dynamic health risk scores using time-varying vitals. It identifies novel interactions in cardiovascular disease risk factors.

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Area of Science:

  • Biostatistics
  • Computational Biology
  • Medical Informatics

Background:

  • High-frequency medical monitoring enables dynamic health risk scores.
  • Survival analysis is suitable for time-varying covariate data.
  • Predicting disease onset requires analyzing continuous health data streams.

Purpose of the Study:

  • Introduce BoXHED software for nonparametric hazard function estimation.
  • Provide the first public software implementation of Lee et al. (2017) estimator.
  • Analyze time-dependent covariates for disease prediction.

Main Methods:

  • Nonparametric hazard estimation using gradient boosting.
  • Tree-based implementation of a generic estimator for time-dependent covariates.
  • Application to Framingham Heart Study cardiovascular disease dataset.

Main Results:

  • BoXHED enables nonparametric estimation of hazard functions.
  • Identified novel interaction effects among cardiovascular disease risk factors.
  • Demonstrated utility in analyzing time-varying health data.

Conclusions:

  • BoXHED is a valuable tool for analyzing dynamic health data.
  • The software may help resolve open questions in clinical literature regarding risk factor interactions.
  • Facilitates advanced survival analysis with time-dependent covariates.