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Related Concept Videos

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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.
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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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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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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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Explained variation under the additive hazards model.

Denise Rava1, Ronghui Xu1,2

  • 1Department of Mathematics, University of California, San Diego, California, USA.

Statistics in Medicine
|October 1, 2020
PubMed
Summary
This summary is machine-generated.

We developed a new method to measure how much variation in survival time is explained by patient factors using the additive hazards model. This approach helps predict mortality risk more accurately in cancer patients.

Keywords:
R2measure of dependencepredictabilitysemiparametric

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Medical Informatics

Background:

  • Quantifying explained variation is crucial in survival analysis for understanding disease progression.
  • The additive hazards regression model offers a flexible framework for analyzing time-to-event data.
  • Existing measures for explained variation may have limitations with right-censored data.

Purpose of the Study:

  • To develop and evaluate a novel measure of explained variation for the additive hazards regression model.
  • To assess the performance of this measure analytically and through simulations.
  • To apply the measure for predicting mortality in early-stage prostate cancer using real-world data.

Main Methods:

  • Investigated different strategies for estimating explained variation.
  • Focused on a measure quantifying the proportion of failure time variation attributable to covariates.
  • Employed analytical derivations and extensive simulation studies.
  • Utilized a well-known survival dataset and the Surveillance, Epidemiology, and End Results-Medicare database.

Main Results:

  • The proposed measure effectively estimates the proportion of variation in failure time explained by covariates.
  • Analytical properties and simulation results demonstrate the measure's reliability.
  • Application to prostate cancer data provided insights into mortality prediction using high-dimensional claims codes.

Conclusions:

  • The developed measure provides a valuable tool for assessing covariate effects in additive hazards models.
  • This method enhances the understanding of factors influencing survival and aids in clinical prediction.
  • The approach is applicable to large-scale health databases for epidemiological research.