epiR (version 2.0.76)

rsu.sep.rb: Surveillance system sensitivity assuming risk-based sampling and varying unit sensitivity

Description

Calculates surveillance system (population-level) sensitivity assuming one-stage, risk-based sampling and varying unit sensitivity using either the binomial or hypergeometric methods.

Usage

rsu.sep.rb(N, rr, ppr, df, pstar, method = "binomial")

Value

A list comprised of five elements:

sep

scalar, the population-level sensitivity estimate.

epi

vector, effective probability of infection estimates.

adj.risk

vector, adjusted risks.

n

vector, sample size by risk group

se.u

a vector of the mean sensitivity for each risk group.

Arguments

N

vector of the same length as rr, population size estimates for each risk group.

rr

vector of length equal to the number of risk strata, the relative risk values.

ppr

vector of the same length as rr, population proportions for each risk group.

df

a dataframe of values for each combination of risk stratum and sensitivity level. Column 1 = risk group index, column 2 = unit sensitivities, column 3 = the sample size for risk group and unit sensitivity).

pstar

scalar, the design prevalence.

method

character string indicating the method to be used. Options are binomial or hypergeometric. See details, below.

Details

If method = binomial N is ignored and values for ppr need to be entered. Conversely, if method = hypergeometric, ppr is ignored and calculated from N.

Examples

## EXAMPLE 1:
## Calculate the surveillance system sensitivity assuming one-stage risk-
## based sampling assuming a population comprised of high risk (n = 200 
## clusters) and low risk (n = 1800 clusters) where the probability of 
## disease in the high risk group is 5 times that of the low risk group.

## Four clusters will be sampled with n = 80, 30, 20 and 30 surveillance
## units within each cluster tested using a test with diagnostic sensitivity
## at the surveillance unit level of 0.92, 0.85, 0.92 and 0.85, respectively.

## Assume a design prevalence of 0.01.

rg <- c(1,1,2,2)
se.u <- c(0.92,0.85,0.92,0.85)
n <- c(80,30,20,30)
df <- data.frame(rg, se.u, n)

rsu.sep.rb(N = c(200,1800), rr = c(5,1), ppr = NA,  df = df, pstar = 0.01, 
   method = "hypergeometric")

## The expected surveillance system sensitivity is 0.993.

 
## EXAMPLE 2:
## Recalculate, assuming that we don't know the size of the cluster population
## at risk.

## When the size of the cluster population at risk is unknown we set N = NA 
## and enter values for ppr (the proportion of the population in each risk
## group). Assume (from above) that 0.10 of the cluster population are in the
## high risk group and 0.90 are in the low risk group.

rsu.sep.rb(N = NA, rr = c(5,1), ppr = c(0.10,0.90), df = df, pstar = 0.01, 
   method = "binomial")

## The expected surveillance system sensitivity is 0.980.