# rsu.epinf: Effective probability of disease In epiR: Tools for the Analysis of Epidemiological Data

## Description

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

## Usage

 `1` ```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

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27``` ```## 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 Oct. 11, 2021, 9:08 a.m.