rsu.sep.rb2rf {epiR} | R Documentation |

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

rsu.sep.rb2rf(N, n, rr1, ppr1, rr2, ppr2, pstar, se.u, method = "binomial")

`N` |
matrix of population sizes for each risk group. Rows = levels of |

`n` |
matrix of the number of surveillance units tested in each risk group. Rows = levels of |

`rr1` |
scalar or vector defining the first set of relative risk values. |

`ppr1` |
scalar or vector of the same length as that vector of |

`rr2` |
matrix defining the relative risks for the second risk factor. Rows = levels of |

`ppr2` |
matrix defining the population proportions in each of the second risk strata. Row proportions must sum to one. Rows = levels of |

`pstar` |
scalar, defining the design prevalence. |

`se.u` |
scalar or vector of the same length as that vector of |

`method` |
character string indicating the method to be used. Options are |

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`

.

A list comprised of two elements:

`se.p` |
scalar, surveillance system (population-level) sensitivity estimates. |

`epi` |
vector, effective probability of infection estimates. |

`adj.risk1` |
vector, adjusted relative risk estimates for the first risk factor. |

`adj.risk2` |
vector, adjusted relative risk estimates for the second risk factor. |

## EXAMPLE 1: ## A cross-sectional study is to be carried out to confirm the absence of ## disease using risk based sampling. Assume a design prevalence of 0.01 ## at the surveillance unit level. Surveillance units are categorised as ## being either high or low risk with the probability of disease for ## high risk surveillance units 3 times the probability of disease for low ## risk units. The proportion of units in each risk group is 0.20 and 0.80, ## respectively. ## Within each of the two risk categories the probability of disease varies ## with age with younger age groups having four times the risk of disease ## as older age groups. In the high risk area 10% of the population are young ## and 90% are old. In the low risk area 30% of the population are young and ## 70% are old. ## The total number of surveillance units in the population is unknown. The ## numbers of young and old surveillance units tested in the high and low risk ## groups are 40, 20, 20 and 10, respectively. You intend to use a test with ## diagnostic sensitivity of 0.80. What is the surveillance system sensitivity? rsu.sep.rb2rf(N = NA, n = rbind(c(40,20), c(20,10)), rr1 = c(3,1), ppr1 = c(0.20,0.80), rr2 = rbind(c(4,1), c(4,1)), ppr2 = rbind(c(0.10,0.90), c(0.30,0.70)), pstar = 0.01, se.u = 0.80, method = "binomial")$se.p ## The surveillance system sensitivity is 0.93. ## EXAMPLE 2: ## This example shows the importance of sampling high risk groups. Take the ## same scenario as above but switch the relative proportions sampled by ## risk group --- taking a greater number of samples from the low risk group ## compared with the high risk group: rsu.sep.rb2rf(N = NA, n = rbind(c(10,20), c(20,40)), rr1 = c(3,1), ppr1 = c(0.20,0.80), rr2 = rbind(c(4,1), c(4,1)), ppr2 = rbind(c(0.10,0.90), c(0.30,0.70)), pstar = 0.01, se.u = 0.80, method = "binomial")$se.p ## The surveillance system sensitivity is 0.69. Here we've taken exactly the ## same number of samples as Example 1, but there's a substantial decrease ## in surveillance system sensitivity because we've concentrated sampling on ## a low risk group (decreasing our ability to detect disease).

[Package *epiR* version 2.0.38 Index]