rsu.sep.rb1rf {epiR} R Documentation

## Surveillance system sensitivity assuming risk-based sampling on one risk factor

### Description

Calculates risk-based surveillance system (population-level) sensitivity with a single risk factor, assuming one-stage risk-based sampling and allowing unit sensitivity to vary among risk strata.

### Usage

```rsu.sep.rb1rf(N, n, rr, ppr, pstar, se.u, method = "binomial")
```

### Arguments

 `N` scalar or vector of the same length as that vector of `rr` defining the population size per risk strata. Ignored if `method = "binomial"`. `n` scalar or vector of the same length as that vector of `rr` defining the sample size per risk strata. `rr` scalar or vector of the same length as that vector of `ppr` defining the relative risk values. `ppr` scalar or vector of the same length as that vector of `rr` defining the population proportions in each risk strata. Ignored if `method = "hypergeometric"`. `pstar` scalar, defining the design prevalence. `se.u` scalar or vector of the same length as that vector of `rr` defining the unit sensitivity (which can vary across strata). `method` character string indicating the method to be used. Options are `binomial` or `hypergeometric`. See details, below.

### Details

If `method = binomial` `N` is ignored and values for `ppr` need to be entered. Conversely, if `method = hypergeometric`, `ppr` is ignored and calculated from `N`.

### Value

A list comprised of two elements:

 `se.p` scalar, surveillance system (population-level) sensitivity estimates. `epi` vector, effective probability of infection estimates. `adj.risk` vector, adjusted relative risk estimates.

### Examples

```## EXAMPLE 1:
## A cross-sectional study is to be carried out to confirm the absence of
## disease using one-stage risk based sampling. Assume a design prevalence of
## 0.10 at the cluster (herd) level and the total number of clusters in
## the population is unknown. Clusters are categorised as being either high,
## medium or low risk with the probability of disease for clusters in the
## high and medium risk area 5 and 3 times the probability of disease in the
## low risk area. The proportions of clusters in the high, medium and low risk
## area are 0.10, 0.10 and 0.80, respectively and you elect to sample five
## clusters from each of the three areas using a test with diagnostic
## sensitivity of 0.90. What is the surveillance system sensitivity?

rsu.sep.rb1rf(N = NA, n = c(5,5,5), rr = c(5,3,1), ppr = c(0.10,0.10,0.80),
pstar = 0.10, se.u = 0.90, method = "binomial")

## The surveillance system sensitivity is 0.94.

## EXAMPLE 2:
## Same scenario as above, but this time assume we know how many clusters are
## in the high, medium and low risk areas: 10, 10 and 80, respectively. What is
## the surveillance system sensitivity?

rsu.sep.rb1rf(N = c(10,10,80), n = c(5,5,5), rr = c(5,3,1), ppr = NA,
pstar = 0.10, se.u = 0.90, method = "hypergeometric")

## The surveillance system sensitivity is 0.96, almost identical to that
## calculated above where the binomial distribution was used to account for
## not knowing the size of the cluster population at risk.

```

[Package epiR version 2.0.38 Index]