besag_newell: Besag-Newell Cluster Detection Method

Description Usage Arguments Details Value Note Author(s) References Examples

View source: R/besag_newell.R

Description

Besag-Newell cluster detection method. There are differences with the original paper and our implementation:

The first two and last differences are because we view the testing on an area-by-area level, rather than a case-by-case level.

Usage

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besag_newell(geo, population, cases, expected.cases = NULL, k, alpha.level)

Arguments

geo

an n x 2 table of the (x,y)-coordinates of the area centroids

population

aggregated population counts for all n areas

cases

aggregated case counts for all n areas

expected.cases

expected numbers of disease for all n areas

k

number of cases to consider

alpha.level

alpha-level threshold used to declare significance

Details

For the population and cases tables, the rows are bunched by areas first, and then for each area, the counts for each strata are listed. It is important that the tables are balanced: the strata information are in the same order for each area, and counts for each area/strata combination appear exactly once (even if zero).

Value

List containing

clusters

information on all clusters that are α-level significant, in decreasing order of the p-value

p.values

for each of the n areas, p-values of each cluster of size at least k

m.values

for each of the n areas, the number of areas need to observe at least k cases

observed.k.values

based on m.values, the actual number of cases used to compute the p-values

Note

The clusters list elements are themselves lists reporting:

location.IDs.included ID's of areas in cluster, in order of distance
population population of cluster
number.of.cases number of cases in cluster
expected.cases expected number of cases in cluster
SMR estimated SMR of cluster
p.value p-value

Author(s)

Albert Y. Kim

References

Besag J. and Newell J. (1991) The Detection of Clusters in Rare Diseases Journal of the Royal Statistical Society. Series A (Statistics in Society), 154, 143–155

Examples

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## Load Pennsylvania Lung Cancer Data
data(pennLC)
data <- pennLC$data

## Process geographical information and convert to grid
geo <- pennLC$geo[,2:3]
geo <- latlong2grid(geo)

## Get aggregated counts of population and cases for each county
population <- tapply(data$population,data$county,sum)
cases <- tapply(data$cases,data$county,sum)

## Based on the 16 strata levels, computed expected numbers of disease
n.strata <- 16
expected.cases <- expected(data$population, data$cases, n.strata)

## Set Parameters
k <- 1250
alpha.level <- 0.05

# not controlling for stratas
results <- besag_newell(geo, population, cases, expected.cases=NULL, k, 
                       alpha.level)

# controlling for stratas
results <- besag_newell(geo, population, cases, expected.cases, k, alpha.level)

SpatialEpi documentation built on July 27, 2021, 9:06 a.m.