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

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

# 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 July 2, 2020, 2:33 a.m.