# sep.rb2.hypergeo: Hypergeometric risk-based population sensitivity for 2 risk... In RSurveillance: Design and Analysis of Disease Surveillance Activities

## Description

Calculates risk-based population sensitivity for two risk factors, using hypergeometric approximation method (assumes a known population size)

## Usage

 `1` ```sep.rb2.hypergeo(pstar, rr1, rr2, N, n, se) ```

## Arguments

 `pstar` design prevalence (scalar) `rr1` relative risks for first level risk factor (vector of values corresponding to the number of risk strata) `rr2` relative risks for second level risk factor, matrix, rows = levels of rr1, cols = levels of rr2 `N` matrix of population size for each risk group (rows = levels of rr1, cols = levels of rr2) `n` matrix of number tested (sample size) for each risk group (rows = levels of rr1, cols = levels of rr2) `se` test unit sensitivity (scalar)

## Value

list of 6 elements, a scalar of population-level sensitivity a matrix of EPI values, a vector of corresponding Adjusted risks for the first risk factor and a matrix of adjusted risks for the second risk factor, a vector of population proportions for the first risk factor and a matrix of population proportions for the second risk factor

## Examples

 ```1 2 3 4 5 6 7 8``` ```# examples for sep.rb2.hypergeo pstar<- 0.01 rr1<- c(3, 1) rr2<- rbind(c(4,1), c(4,1)) N<- rbind(c(100, 500), c(300, 1000)) n<- rbind(c(50, 20), c(20, 10)) se<- 0.8 sep.rb2.hypergeo(pstar, rr1, rr2, N, n, se) ```

RSurveillance documentation built on May 29, 2017, 11:52 p.m.