epiR (version 0.9-11)

epi.prev: Estimate true prevalence

Description

Computes the true prevalence of a disease in a population on the basis of an imperfect test.

Usage

epi.prev(pos, tested, N, se, sp, conf.level = 0.95)

Arguments

pos

the number of positives.

tested

the number tested.

N

the size of the population of interest.

se

test sensitivity (0 - 1).

sp

test specificity (0 - 1).

conf.level

magnitude of the returned confidence interval. Must be a single number between 0 and 1.

Value

• A data frame with four elements: crude: the apparent prevalence, est: the estimated true prevalence, lower: the lower bounds of the confidence interval of the estimated prevalence, and upper: the upper bounds of the confidence interval of the estimated prevalence.

References

Rogan W, Gladen B (1978). Estimating prevalence from results of a screening test. American Journal of Epidemiology 107: 71 - 76. Rothman KJ (2002). Epidemiology An Introduction. Oxford University Press, London, pp. 130 - 143.

Examples

## A simple random sample of 150 cows from a herd of 2560 is taken.
## Each cow is given a screening test for brucellosis which has a 
## sensitivity of 96\% and a specificity of 89\%. Of the 150 cows tested
## 23 were positive to the screening test. What is the estimated prevalence 
## of brucellosis in this herd (and its 95\% confidence interval)?

epi.prev(23, 150, 2560, se = 0.96, sp = 0.89, conf.level = 0.05)

## The estimated true prevalence of brucellosis in this herd is 5.1 cases per 
## 100 cows (95\% CI 0 -- 12 cases per 100 cows).