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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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Tutorial on kernel estimation of continuous spatial and spatiotemporal relative risk.

Tilman M Davies1, Jonathan C Marshall2, Martin L Hazelton2

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

Statistics in Medicine
|December 12, 2017
PubMed
Summary
This summary is machine-generated.

Kernel smoothing methods are essential for analyzing spatial data, estimating relative risk surfaces, and have broad applications beyond epidemiology. This tutorial reviews current techniques and introduces the sparr R package for their implementation.

Keywords:
adaptive bandwidthgeographical epidemiologykernel density estimationspatial point patterntolerance contours

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Area of Science:

  • Spatial statistics
  • Epidemiology
  • Geographical data analysis

Background:

  • Kernel smoothing is a flexible method for estimating probability density and intensity functions in spatial data.
  • It is crucial for calculating relative risk surfaces, particularly in epidemiological studies.
  • Its applications extend to various fields requiring spatial density comparisons.

Purpose of the Study:

  • To provide a comprehensive review of current kernel smoothing methodologies for spatial data analysis.
  • To highlight recent advancements in estimation, computation, and inference for relative risk surfaces.
  • To introduce and demonstrate the use of the sparr R package for implementing these techniques.

Main Methods:

  • Review of kernel smoothing techniques for spatial density estimation.
  • Discussion of spatially adaptive smoothers and asymptotic theory for risk assessment.
  • Integration of novel computational methods for evaluating spatial functionals.
  • Implementation of all discussed methods within the sparr R package.

Main Results:

  • The paper reviews established and novel kernel smoothing methods for spatial analysis.
  • It covers advancements in adaptive smoothing, risk testing, spatiotemporal extensions, and computational approaches.
  • The sparr R package provides a unified platform for applying these methods.

Conclusions:

  • Kernel smoothing is a versatile tool for spatial density and relative risk estimation across disciplines.
  • Ongoing methodological developments enhance its applicability and accuracy.
  • The sparr R package facilitates practical implementation and application in epidemiological research.