Risk-based sample size for varying unit sensitivity

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Description

Calculates sample size for risk-based sampling for a single risk factor and varying unit sensitivity, using binomial method

Usage

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n.rb.varse(pstar, rr, ppr, spr, se, spr.rg, sep)

Arguments

pstar

design prevalence (scalar)

rr

relative risk values (vector, length equal to the number of risk strata)

ppr

population proportions for each risk group, vector of same length as rr

spr

planned surveillance proportions for each risk group, vector of same length as rr

se

unit sensitivities (vector of group values)

spr.rg

proportions of samples for each sensitivity value in each risk group (matrix with rows = risk groups, columns = sensitivity values), row sums must equal 1

sep

required population sensitivity (scalar)

Value

list of 3 elements, a matrix of sample sizes for each risk and sensitivity group, a vector of EPI values and a vector of mean sensitivity for each risk group

Examples

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# examples for n.rb.varse
m<- rbind(c(0.8, 0.2), c(0.5, 0.5), c(0.7, 0.3))
n.rb.varse(0.01, c(5, 3, 1), c(0.1, 0.1, 0.8), c(0.4, 0.4, 0.2), c(0.92, 0.8), m, 0.95)

m<- rbind(c(0.8, 0.2), c(0.6, 0.4))
n.rb.varse(0.05, c(3, 1), c(0.2, 0.8), c(0.7, 0.3), c(0.95, 0.8), m, 0.95)

m<- rbind(c(1), c(1))
n.rb.varse(0.05, c(3, 1), c(0.2, 0.8), c(0.7, 0.3), c(0.95), m, 0.99)

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