rsu.sep.rsmult {epiR}R Documentation

Surveillance system sensitivity by combining multiple surveillance components

Description

Calculates surveillance system (population-level) sensitivity for multiple components, accounting for lack of independence (overlap) between components.

Usage

rsu.sep.rsmult(C = NA, pstar.c, rr, ppr, se.c)

Arguments

C

scalar integer or vector of the same length as rr, representing the population sizes (number of clusters) for each risk group.

pstar.c

scalar (0 to 1) representing the cluster level design prevalence.

rr

vector of length equal to the number of risk strata, representing the cluster relative risks.

ppr

vector of the same length as rr representing the cluster level population proportions. Ignored if C is specified.

se.c

surveillance system sensitivity estimates for clusters in each component and corresponding risk group. A list with multiple elements where each element is a dataframe of population sensitivity values from a separate surveillance system component. The first column equals the clusterid, the second column equals the cluster-level risk group index and the third column equals the population sensitivity values.

Value

A list comprised of two elements:

se.p

a matrix (or vector if C is not specified) of population-level (surveillance system) sensitivities (binomial and hypergeometric and adjusted vs unadjusted).

se.component

a matrix of adjusted and unadjusted sensitivities for each component.

Examples

## EXAMPLE 1:
## You are working with a population that is comprised of indviduals in 
## 'high' and 'low' risk area. There are 300 individuals in the high risk  
## area and 1200 individuals in the low risk area. The risk of disease for 
## those in the high risk area is assumed to be three times that of the low
## risk area.

C <- c(300,1200)
pstar.c <- 0.01
rr <- c(3,1)

## Generate population sensitivity values for clusters in each component of
## the surveillance system. Each of the three dataframes below lists id, 
## rg (risk group) and cse (component sensitivity):

comp1 <- data.frame(id = 1:100, 
   rg = c(rep(1,time = 50), rep(2, times = 50)), 
   cse = rep(0.5, times = 100)) 

comp2 <- data.frame(id = seq(from = 2, to = 120, by = 2), 
   rg = c(rep(1, times = 25), rep(2, times = 35)), 
   cse = runif(n = 60, min = 0.5, max = 0.8))

comp3 <- data.frame(id = seq(from = 5, to = 120, by = 5), 
   rg = c(rep(1, times = 10), rep(2, times = 14)), 
   cse = runif(n = 24, min = 0.7, max = 1))
 
# Combine the three components into a list:  
se.c <- list(comp1, comp2, comp3)

## What is the overall population-level (surveillance system) sensitivity?

rsu.sep.rsmult(C = C, pstar.c = pstar.c, rr = rr, ppr = NA, se.c = se.c)

## The overall adjusted system sensitivity (calculated using the binomial
## distribution) is 0.85.  


## EXAMPLE 2:
## Assume that you don't know exactly how many individuals are in the high 
## and low risk areas but you have a rough estimate that the proportion of
## the population in each area is 0.2 and 0.8, respectively. What is the 
## population-level (surveillance system) sensitivity?

ppr <- c(0.20,0.80)

rsu.sep.rsmult(C = NA, pstar.c = pstar.c, rr = rr, ppr = ppr, se.c = se.c)

## The overall adjusted system sensitivity (calculated using the binomial
## distribution) is 0.85.


[Package epiR version 2.0.38 Index]