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

Calculates surveillance system (population-level) sensitivity assuming one-stage, risk-based sampling and varying unit sensitivity using either the binomial or hypergeometric methods.

rsu.sep.rb(N, rr, ppr, df, pstar, method = "binomial")

`N` |
vector of the same length as |

`rr` |
vector of length equal to the number of risk strata, the relative risk values. |

`ppr` |
vector of the same length as |

`df` |
a dataframe of values for each combination of risk stratum and sensitivity level. Column 1 = risk group index, column 2 = unit sensitivities, column 3 = the sample size for risk group and unit sensitivity). |

`pstar` |
scalar, the design prevalence. |

`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 five elements:

`sep` |
scalar, the population-level sensitivity estimate. |

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

`adj.risk` |
vector, adjusted risks. |

`n` |
vector, sample size by risk group |

`se.u` |
a vector of the mean sensitivity for each risk group. |

## EXAMPLE 1: ## Calculate the surveillance system sensitivity assuming one-stage risk- ## based sampling assuming a population comprised of high risk (n = 200 ## clusters) and low risk (n = 1800 clusters) where the probability of ## disease in the high risk group is 5 times that of the low risk group. ## Four clusters will be sampled with n = 80, 30, 20 and 30 surveillance ## units within each cluster tested using a test with diagnostic sensitivity ## at the surveillance unit level of 0.92, 0.85, 0.92 and 0.85, respectively. ## Assume a design prevalence of 0.01. rg <- c(1,1,2,2) se.u <- c(0.92,0.85,0.92,0.85) n <- c(80,30,20,30) df <- data.frame(rg, se.u, n) rsu.sep.rb(N = c(200,1800), rr = c(5,1), ppr = NA, df = df, pstar = 0.01, method = "hypergeometric") ## The expected surveillance system sensitivity is 0.993. ## EXAMPLE 2: ## Recalculate, assuming that we don't know the size of the cluster population ## at risk. ## When the size of the cluster population at risk is unknown we set N = NA ## and enter values for ppr (the proportion of the population in each risk ## group). Assume (from above) that 0.10 of the cluster population are in the ## high risk group and 0.90 are in the low risk group. rsu.sep.rb(N = NA, rr = c(5,1), ppr = c(0.10,0.90), df = df, pstar = 0.01, method = "binomial") ## The expected surveillance system sensitivity is 0.980.

[Package *epiR* version 2.0.38 Index]