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Related Concept Videos

Relative Risk01:12

Relative Risk

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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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Shrinkage in concrete is primarily due to water loss from evaporation, hydration of cement, or carbonation, leading to a reduction in volume. The volumetric contraction results in volumetric strain in concrete. However, in practice, shrinkage is measured as linear strain, which is one-third of the volumetric strain.
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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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Hazard Ratio01:12

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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 vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
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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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An R-Based Landscape Validation of a Competing Risk Model
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Shrinkage estimators of the spatial relative risk function.

Martin L Hazelton1

  • 1Department of Mathematics and Statistics, University of Otago, Dunedin, New Zealand.

Statistics in Medicine
|August 20, 2023
PubMed
Summary

This study introduces a new lasso-type estimator to improve spatial relative risk function analysis in epidemiology. The method enhances the stability of risk hotspot identification, even in areas with sparse data.

Keywords:
cross-validationkernel smoothinglassolocal likelihoodpenalization

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Area of Science:

  • Spatial statistics
  • Epidemiology
  • Geographical analysis

Background:

  • Spatial relative risk (SRR) function quantifies geographical differences in point distributions, crucial for epidemiological studies.
  • Estimating SRR using kernel density methods faces challenges due to spatial inhomogeneity, leading to unstable risk estimates in sparse data areas.
  • Distinguishing true risk hotspots from random variations in SRR estimates remains difficult with traditional methods.

Purpose of the Study:

  • To develop improved methods for estimating the spatial relative risk function.
  • To address challenges in identifying stable and accurate risk hotspots, especially in areas with limited data.
  • To introduce a novel shrinkage estimator for more reliable spatial risk assessment.

Main Methods:

  • Proposed a new lasso-type shrinkage estimator for the log-spatial relative risk function.
  • The estimator shrinks a standard kernel estimator towards zero, controlled by a tuning parameter.
  • Evaluated the estimator's performance using simulation studies and real-world epidemiological data.

Main Results:

  • The proposed lasso estimator demonstrated encouraging performance in both simulated and real-world analyses.
  • Shrinkage effectively stabilizes estimates in sparse data regions while preserving detail elsewhere.
  • The tuning parameter allows for quantifying evidence of risk hotspots or optimizing via cross-validation.

Conclusions:

  • Shrinkage estimators, particularly the novel lasso-type method, offer a robust approach to estimating spatial relative risk functions.
  • This method improves the identification of true risk hotspots, overcoming limitations of traditional kernel estimation techniques.
  • The lasso estimator provides a valuable tool for epidemiological research requiring accurate spatial risk assessment.