rsu.sep.rb2st {epiR}R Documentation

Surveillance system sensitivity assuming risk based, two-stage sampling

Description

Calculates the surveillance system sensitivity for detection of disease assuming risk based, two-stage sampling (sampling of clusters and sampling of units within clusters), imperfect test sensitivity and perfect test specificity. The method allows for a single risk factor at each stage.

Usage

rsu.sep.rb2st(H = NA, N = NA, n, rr.c, ppr.c, pstar.c, rr.u, ppr.u, 
   pstar.u, rg, se.u)

Arguments

H

scalar, integer representing the total number of clusters in the population. Use NA if unknown.

N

vector, integer representing the number of surveillance units within each cluster. Use NA if unknown.

n

vector, integer representing the number of surveillance units tested within each cluster.

rr.c

cluster level relative risks (vector of length corresponding to the number of risk strata), use rr.c = c(1,1) if a risk factor does not apply.

ppr.c

vector listing the cluster level population proportions for each risk category. Use NA if there are no cluster level risk factors.

pstar.c

scalar, numeric (0 to 1) the cluster-level design prevalence.

rr.u

surveillance unit level relative risks (vector of length corresponding to the number of risk strata), use rr.u = c(1,1) if a risk factor does not apply.

ppr.u

matrix providing the surveillance unit level population proportions for each risk group. One row for each cluster, columns = unit level risk groups, not required if N is provided.

pstar.u

scalar, numeric (0 to 1) the unit-level design prevalence.

rg

vector, listing the risk group (index) for each cluster.

se.u

scalar, numeric (0 to 1), representing the sensitivity of the diagnostic test at the individual surveillance unit level.

Value

A list comprised of:

se.p

the surveillance system (population-level) sensitivity of detection.

se.c

the cluster-level sensitivity of detection.

Examples

## EXAMPLE 1:
## You have been asked to provide an assessment of a surveillance program 
## for Actinobacillus hyopneumoniae in pigs. It is known that there are 
## high risk and low risk areas for A. hypopneumoniae in your country with 
## the estimated probability of disease in the high risk area thought to 
## be around 3.5 times that of the probability of disease in the low risk area. 
## It is known that 10% of the 1784 pig herds in the study area are in the 
## high risk area and 90% are in the low risk area.

## The risk of A. hypopneumoniae is dependent on age, with adult pigs around 
## five times more likely to be A. hypopneumoniae positive compared with 
## younger (grower) pigs. 

## Pigs from 20 herds have been sampled: 5 from the low-risk area and 15 from 
## the high-risk area. All of the tested pigs were adults: no grower pigs 
## were tested. 

## The ELISA for A. hypopneumoniae in pigs has a diagnostic sensitivity 
## of 0.95.

## What is the surveillance system sensitivity if we assume a design 
## prevalence of 1 per 100 at the cluster (herd) level and 5 per 100 
## at the surveillance system unit (pig) level?

# There are 1784 herds in the study area:

H <- 1784

# Twenty of the 1784 herds are sampled. Generate 20 herds of varying size:
set.seed(1234)

hsize <- rlnorm(n = 20, meanlog = log(10), sdlog = log(8))
hsize <- round(hsize + 20, digits = 0)

# Generate a matrix listing the number of growers and finishers in each of 
## the 20 sampled herds. Anywhere between 80% and 95% of the animals in 
## each herd are growers:

set.seed(1234)
pctg <- runif(n = 20, min = 0.80, max = 0.95)
ngrow <- round(pctg * hsize, digits = 0)
nfini <- hsize - ngrow
N <- cbind(ngrow, nfini)

# Generate a matrix listing the number of grower and finisher pigs sampled 
## from each herd:

nsgrow <- rep(0, times = 20)
nsfini <- ifelse(nfini <= 15, nfini, 15)
n <- cbind(nsgrow, nsfini)

# The herd-level design prevalence is 0.01 and the individual pig-level design 
## prevalence is 0.05: 

pstar.c <- 0.01
pstar.u <- 0.05

# For herds in the high-risk area the probability being A. hyopneumoniae 
## positive is 3.5 times that of herds in the low-risk area. Ninety 
## percent of herds are in the low risk area and 10% are in the high risk area:

rr.c <- c(1,3.5)
ppr.c <- c(0.9,0.1) 

## We've sampled 5 herds from the low risk area and 15 herds from the 
## high risk area:

rg <- c(rep(1, times = 5), rep(2, times = 15))

## For finishers the probability being A. hyopneumoniae positive is 5 times 
## that of growers:

rr.u <- c(1,5)

## The diagnostic sensitivity of the A. hyopneumoniae ELISA is 0.95:

se.u <- 0.95

rsu.sep.rb2st(H = H, N = N, n = n, 
   pstar.c = pstar.c, pstar.u = pstar.u,
   rg = rg, rr.c = rr.c, rr.u = rr.u,
   ppr.c = ppr.c, ppr.u = NA,
   se.u = se.u)

## The estimated surveillance system sensitivity of this program is 0.31. 


## EXAMPLE 2:
## Repeat these analyses assuming we don't know the total number of pig herds 
## in the population and we have only an estimate of the proportions of 
## growers and finishers in each herd. 

## Generate a matrix listing the proportion of growers and finishers in each
## of the 20 sampled herds:

ppr.u <- cbind(rep(0.9, times = 20), rep(0.1, times = 20))

# Set H (the number of clusters) and N (the number of surveillance units 
## within each cluster) to NA:

rsu.sep.rb2st(H = NA, N = NA, n = n, 
   pstar.c = pstar.c, pstar.u = pstar.u,
   rg = rg, rr.c = rr.c, rr.u = rr.u,
   ppr.c = ppr.c, ppr.u = ppr.u,
   se.u = se.u)

## The estimated surveillance system sensitivity is 0.20.

[Package epiR version 2.0.31 Index]