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 2.0.31 Index]