rsu.sep.rbvarse {epiR} | R Documentation |
Calculates the surveillance system (population-level) sensitivity for detection of disease assuming risk based sampling and varying unit sensitivity.
rsu.sep.rbvarse(N, rr, df, pstar)
N |
scalar integer or vector of integers the same length as |
rr |
relative risk values (vector of values corresponding to the number of risk strata). |
df |
dataframe of values for each combination of risk stratum and sensitivity level, column 1 = risk group index, column 2 = unit sensitivity, column 3 = n (sample size for risk group and unit sensitivity). |
pstar |
scalar representing the design prevalence. |
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. |
MacDiarmid S (1988). Future options for brucellosis surveillance in New Zealand beef herds. New Zealand Veterinary Journal 36: 39 - 42.
Martin S, Shoukri M, Thorburn M (1992). Evaluating the health status of herds based on tests applied to individuals. Preventive Veterinary Medicine 14: 33 - 43.
## EXAMPLE 1: ## A study has been carried out to detect Johne's disease in a population of ## cattle. There are two risk groups ('high' and 'low') with the risk of ## disease in the high risk group five times that of the low risk group. ## The number of animals sampled and unit sensitivity varies by risk group, as ## detailed below. Assume there number of cattle in the high risk and low risk ## group is 200 and 1800, respectively. ## Calculate the surveillance system sensitivity assuming 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 = rg, se.u = se.u, n = n) rsu.sep.rbvarse(N = c(200,1800), rr = c(5,1), df = df, pstar = 0.01) ## The surveillance system sensitivity is 0.99.