Computes the optimal sample size for a survey to substantiate
freedom from disease. The optimal sample size is the smallest
sample size that produces an alpha-error less than or equal
to a prediscribed value `alpha`

. The population is considered
as diseased if at least one individual has a positive test result.
The sample size is computed using a bisection method.
The function either computes the sample size for a fixed population
(`lookupTable == FALSE`

) or a lookup table with sampling sizes
depending on the population size for individual sampling
(`lookupTable == TRUE`

); see Ziller et al., 2002.

1 2 | ```
computeOptimalSampleSize(nPopulation, prevalence, alpha = 0.05,
sensitivity = 1, specificity = 1, lookupTable = FALSE)
``` |

`nPopulation` |
Integer. Population size if |

`prevalence` |
Numeric between 0 and 1. Design prvalence. The number of diseased
is then computed as |

`alpha` |
Numeric between 0 and 1. Alpha-Error (=error of the first kind, significance level) of the underlying significance test. Default value = 0.05. |

`sensitivity` |
Numeric between 0 and 1. Sensitivity of the diagnostic (for one-stage sampling) or herd test (for two stage sampling). Default value = 1. |

`specificity` |
Numeric between 0 and 1. Specificity of the diagnostic (for one-stage sampling) or herd test (for two stage sampling). Default value = 1. |

`lookupTable` |
Logical. TRUE if a lookup table of sample sizes for individual sampling (see, Ziller et al., 2002) should be produced. FALSE if the sample size is desired for a fixed population size (default). |

The return value is either an integer, the minimal sample size that produces
the desired alpha-error if `lookupTable == FALSE`

or a matrix with
columns `N_lower`

, `N_upper`

, `sampleSize`

containing
the sample sizes for the different herd sizes `N`

.

E.g., `N_lower = 17`

, `N_upper = 21`

, `sampleSize = 11`

means
that for holdings with 17-21 animals 11 animals need to be tested in order
to achieve the desired accuracy (=herd sensitivity).

Ian Kopacka <ian.kopacka@ages.at>

A.R. Cameron and F.C. Baldock, "A new probablility formula to substantiate freedom from disease", Prev. Vet. Med. 34 (1998), pp. 1-17.

M. Ziller, T. Selhorst, J. Teuffert, M. Kramer and H. Schlueter, "Analysis of sampling strategies to substantiate freedom from disease in large areas", Prev. Vet. Med. 52 (2002), pp. 333-343.

`computePValue`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | ```
## Compute the number of herds to be tested for limited sampling
## with sampleSizeLtd = 7:
#################################################################
data(sheepData)
## Compute the average herd sensitivity:
alphaList <- computeAlphaLimitedSampling(stockSizeVector =
sheepData$nSheep, sampleSizeLtd = 7,
intraHerdPrevalence = 0.13, diagSensitivity = 0.9,
diagSpecificity = 1)
sensHerd <- 1 - alphaList$meanAlpha
## Number of herds to be tested:
nHerds <- computeOptimalSampleSize(nPopulation = dim(sheepData)[1],
prevalence = 0.002, alpha = 0.05, sensitivity = sensHerd,
specificity = 1)
## Compute the number of animals to be tested for individual
## sampling:
#################################################################
sampleSizeIndividual <- computeOptimalSampleSize(nPopulation = 300,
prevalence = 0.2, alpha = 0.05, sensitivity = 0.97,
specificity = 1, lookupTable = TRUE)
``` |

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

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