Description Usage Arguments Value Examples

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

1 |

`pstar` |
scalar, the design prevalence. |

`rr` |
vector, defining the relative risk values for each strata in the population. |

`ppr` |
vector of length |

A list of comprised of two elements:

`epinf` |
a vector listing the effective probability of infection listed in order of |

`adj.risk` |
a vector listing the adjusted risk values listed in order of |

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.
``` |

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