Statistical Significance: Tests for Spatial Randomness

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Statistical SignificanceTests for Spatial
Randomness
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Whether or not there are true geographical
differences in risk, there will always be some
geographical patterns apparent to the naked eye.
As in all medical research, it is important to
evaluate whether observed patterns/results are
likely to be due to chance or not.
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Breast Cancer Incidence, Relative
Risks Age-Adjusted, Indirect Standardization
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Brain Cancer Mortality, Children 1986-1995
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Brain Cancer Mortality, Adults 1986-1995
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Tests for Spatial Randomness
Null Hypothesis The risk of disease is the same
in all parts of the map.
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Covariate Adjustments
For incidence and mortality analyses, it is
important to adjust for age, and sometimes for
other variables as well. This is done using
indirect standardization, so that
a covariate-adjusted expected number of cases are
obtained for each census area. Can be used
with any test for spatial randomness.
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Tests for Spatial Randomness
Three Different Types

• Global Clustering Tests
• Cluster Detection Tests
• Focused Tests

Complementary. Used for different purposes.
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Global Clustering Tests
Evaluates whether clustering exist as a global
phenomena throughout the map, without
pinpointing the location of specific clusters.
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Global Clustering Tests

• Morans I, 1950
• Mantel-Bailars Test, 1970
• Cuzick-Edwards k-NN Test, 1990
• Tangos Maximized Excess Events Test, 2000
• etc.

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Cluster Detection Tests
Determine the location and statistical
significance of clusters without prior
assumptions about their locations.
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Cluster Detection Tests

• Turnbulls CEPP, 1990
• Spatial Scan Statistic, 1995
• etc.

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Focused Tests
Determine whether there is a cluster around a
pre-specified point or linear feature.
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Focused Tests

• Fixed Cut-Off, Lyon et al. 1981
• Isotonic Regression, Stone 1988
• Lawson-Wallers Score Test, 1992,93
• Bithells Linear Rank Score Test, 1995
• etc.