rsu.sep.rb2st {epiR} | R Documentation |

Calculates the surveillance system sensitivity for detection of disease assuming risk based, two-stage sampling (sampling of clusters and sampling of units within clusters), imperfect test sensitivity and perfect test specificity. The method allows for a single risk factor at each stage.

rsu.sep.rb2st(H = NA, N = NA, n, rr.c, ppr.c, pstar.c, rr.u, ppr.u, pstar.u, rg, se.u)

`H` |
scalar, integer representing the total number of clusters in the population. Use |

`N` |
vector, integer representing the number of surveillance units within each cluster. Use |

`n` |
vector, integer representing the number of surveillance units tested within each cluster. |

`rr.c` |
cluster level relative risks (vector of length corresponding to the number of risk strata), use |

`ppr.c` |
vector listing the cluster level population proportions for each risk category. Use |

`pstar.c` |
scalar, numeric (0 to 1) the cluster-level design prevalence. |

`rr.u` |
surveillance unit level relative risks (vector of length corresponding to the number of risk strata), |

`ppr.u` |
matrix providing the surveillance unit level population proportions for each risk group. One row for each cluster, columns = unit level risk groups, not required if |

`pstar.u` |
scalar, numeric (0 to 1) the unit-level design prevalence. |

`rg` |
vector, listing the risk group (index) for each cluster. |

`se.u` |
scalar, numeric (0 to 1), representing the sensitivity of the diagnostic test at the individual surveillance unit level. |

A list comprised of:

`se.p` |
the surveillance system (population-level) sensitivity of detection. |

`se.c` |
the cluster-level sensitivity of detection. |

## EXAMPLE 1: ## You have been asked to provide an assessment of a surveillance program ## for Actinobacillus hyopneumoniae in pigs. It is known that there are ## high risk and low risk areas for A. hypopneumoniae in your country with ## the estimated probability of disease in the high risk area thought to ## be around 3.5 times that of the probability of disease in the low risk area. ## It is known that 10% of the 1784 pig herds in the study area are in the ## high risk area and 90% are in the low risk area. ## The risk of A. hypopneumoniae is dependent on age, with adult pigs around ## five times more likely to be A. hypopneumoniae positive compared with ## younger (grower) pigs. ## Pigs from 20 herds have been sampled: 5 from the low-risk area and 15 from ## the high-risk area. All of the tested pigs were adults: no grower pigs ## were tested. ## The ELISA for A. hypopneumoniae in pigs has a diagnostic sensitivity ## of 0.95. ## What is the surveillance system sensitivity if we assume a design ## prevalence of 1 per 100 at the cluster (herd) level and 5 per 100 ## at the surveillance system unit (pig) level? # There are 1784 herds in the study area: H <- 1784 # Twenty of the 1784 herds are sampled. Generate 20 herds of varying size: set.seed(1234) hsize <- rlnorm(n = 20, meanlog = log(10), sdlog = log(8)) hsize <- round(hsize + 20, digits = 0) # Generate a matrix listing the number of growers and finishers in each of ## the 20 sampled herds. Anywhere between 80% and 95% of the animals in ## each herd are growers: set.seed(1234) pctg <- runif(n = 20, min = 0.80, max = 0.95) ngrow <- round(pctg * hsize, digits = 0) nfini <- hsize - ngrow N <- cbind(ngrow, nfini) # Generate a matrix listing the number of grower and finisher pigs sampled ## from each herd: nsgrow <- rep(0, times = 20) nsfini <- ifelse(nfini <= 15, nfini, 15) n <- cbind(nsgrow, nsfini) # The herd-level design prevalence is 0.01 and the individual pig-level design ## prevalence is 0.05: pstar.c <- 0.01 pstar.u <- 0.05 # For herds in the high-risk area the probability being A. hyopneumoniae ## positive is 3.5 times that of herds in the low-risk area. Ninety ## percent of herds are in the low risk area and 10% are in the high risk area: rr.c <- c(1,3.5) ppr.c <- c(0.9,0.1) ## We've sampled 5 herds from the low risk area and 15 herds from the ## high risk area: rg <- c(rep(1, times = 5), rep(2, times = 15)) ## For finishers the probability being A. hyopneumoniae positive is 5 times ## that of growers: rr.u <- c(1,5) ## The diagnostic sensitivity of the A. hyopneumoniae ELISA is 0.95: se.u <- 0.95 rsu.sep.rb2st(H = H, N = N, n = n, pstar.c = pstar.c, pstar.u = pstar.u, rg = rg, rr.c = rr.c, rr.u = rr.u, ppr.c = ppr.c, ppr.u = NA, se.u = se.u) ## The estimated surveillance system sensitivity of this program is 0.31. ## EXAMPLE 2: ## Repeat these analyses assuming we don't know the total number of pig herds ## in the population and we have only an estimate of the proportions of ## growers and finishers in each herd. ## Generate a matrix listing the proportion of growers and finishers in each ## of the 20 sampled herds: ppr.u <- cbind(rep(0.9, times = 20), rep(0.1, times = 20)) # Set H (the number of clusters) and N (the number of surveillance units ## within each cluster) to NA: rsu.sep.rb2st(H = NA, N = NA, n = n, pstar.c = pstar.c, pstar.u = pstar.u, rg = rg, rr.c = rr.c, rr.u = rr.u, ppr.c = ppr.c, ppr.u = ppr.u, se.u = se.u) ## The estimated surveillance system sensitivity is 0.20.

[Package *epiR* version 2.0.38 Index]