rsu.epinf: Effective probability of disease

In epiR: Tools for the Analysis of Epidemiological Data

Effective probability of disease

Description

Calculates the effective probability of disease (adjusted design prevalence) for each risk group within a population.

Usage

rsu.epinf(pstar, rr, ppr)

Arguments

pstar	scalar, the design prevalence.
rr	vector, defining the relative risk values for each strata in the population.
ppr	vector of length rr defining the population proportions in each strata.

Value

A list of comprised of two elements:

epinf	a vector listing the effective probability of infection listed in order of rr.
adj.risk	a vector listing the adjusted risk values listed in order of rr.

Examples

## EXAMPLE 1:
## For a given disease of interest you believe that there is a 'high risk'
## and 'low risk' area in your country. The risk of disease in the high risk
## area compared with the low risk area is 5. A recent census shows that 
## 10% of the population are resident in the high risk area and 90% 
## are resident in the low risk area. You elect to set a design prevalence 
## of 0.10.

## Calculate the effective probability of infection for each area. 

rsu.epinf(pstar = 0.1, rr = c(5,1), ppr = c(0.10,0.90))

## The effective probabilities of infection for the high and low risk areas 
## are 0.36 and 0.07, respectively.


## EXAMPLE 2:
## Re-calculate the effective probabilities of infection assuming there are 
## 'high', 'medium' and 'low' risk areas. The risk of disease in the 
## medium risk area compared with the low risk area is 3. Population 
## proportions for each area are 0.10, 0.10 and 0.80, respectively.

rsu.epinf(pstar = 0.10, rr = c(5,3,1), ppr = c(0.10,0.10,0.80)) 
 
## The effective probabilities of infection for the high, medium and low 
## risk areas are 0.31, 0.19 and 0.06, respectively.
 

epiR documentation built on Sept. 30, 2024, 9:16 a.m.