# sep.rb.bin.varse: Binomial risk-based population sensitivity for varying unit... In RSurveillance: Design and Analysis of Disease Surveillance Activities

## Description

Calculates population sensitivity for a single risk factor and varying unit sensitivity using binomial method (assumes large population)

## Usage

 `1` ```sep.rb.bin.varse(pstar, rr, ppr, df) ```

## Arguments

 `pstar` design prevalence (scalar) `rr` relative risk values (vector of values corresponding to the number of risk strata) `ppr` population proportions corresponding to rr values (vector of equal length to rr) `df` dataframe of values for each combination of risk stratum and sensitivity level, col 1 = risk group index, col 2 = unit Se, col 3 = n (sample size for that risk group and unit sensitivity)

## Value

list of 3 elements, a scalar of population-level sensitivity a vector of EPI values and a vector of corresponding adjusted risks

## 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``` ```# examples for sep.rb.bin.varse rg<- c(1, 1, 2, 2) se<- c(0.92, 0.85, 0.92, 0.85) n<- c(80, 30, 20, 30) df<- data.frame(rg, se, n) sep.rb.bin.varse(0.01, c(5, 1), c(0.1, 0.9), df) rg<- c(1, 1, 2, 2) se<- c(0.95, 0.8, 0.95, 0.8) n<- c(20, 10, 10, 5) df<- data.frame(rg, se, n) sep.rb.bin.varse(0.05, c(3, 1), c(0.2, 0.8), df) rg<- c(rep(1, 30), rep(2, 15)) se<- c(rep(0.95, 20), rep(0.8, 10), rep(0.95, 10), rep(0.8, 5)) n<- rep(1, 45) df<- data.frame(rg, se, n) sep.rb.bin.varse(0.02, c(3, 1), c(0.2, 0.8), df) rg<- c(1, 2, 3, 1, 2, 3) se<- c(0.95, 0.95, 0.95, 0.8, 0.8, 0.8) n<- c(20, 10, 10, 30, 5, 5) df<- data.frame(rg, se, n) sep.rb.bin.varse(0.01, c(5, 3, 1), c(0.1, 0.3, 0.6), df) ```

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