| Title: | Design and Analysis of Disease Surveillance Activities |
|---|---|
| Description: | A range of functions for the design and analysis of disease surveillance activities. These functions were originally developed for animal health surveillance activities but can be equally applied to aquatic animal, wildlife, plant and human health surveillance activities. Utilities are included for sample size calculation and analysis of representative surveys for disease freedom, risk-based studies for disease freedom and for prevalence estimation. This package is based on Cameron A., Conraths F., Frohlich A., Schauer B., Schulz K., Sergeant E., Sonnenburg J., Staubach C. (2015). R package of functions for risk-based surveillance. Deliverable 6.24, WP 6 - Decision making tools for implementing risk-based surveillance, Grant Number no. 310806, RISKSUR (<https://www.fp7-risksur.eu/sites/default/files/documents/Deliverables/RISKSUR_%28310806%29_D6.24.pdf>). Many of the 'RSurveillance' functions are incorporated into the 'epitools' website: Sergeant, ESG, 2019. Epitools epidemiological calculators. Ausvet Pty Ltd. Available at: <http://epitools.ausvet.com.au>. |
| Authors: | Evan Sergeant |
| Maintainer: | Rohan Sadler <[email protected]> |
| License: | GPL-2|GPL-3 |
| Version: | 0.2.1 |
| Built: | 2024-11-10 06:02:22 UTC |
| Source: | https://github.com/roStats/RSurveillance |
Calculates adjusted risk for given relative risk and population proportions. This is an intermediate calculation in the calculation of effective probability of infection for risk-based surveillance activities
adj.risk(rr, ppr)adj.risk(rr, ppr)
rr |
relative risk values (vector of values corresponding to the number of risk strata) |
ppr |
population proportions corresponding to rr values (vector of equal length to rr) |
vector of adjusted risk values (in order corresponding to rr)
# examples for adj.risk adj.risk(c(5, 1), c(0.1, 0.9)) adj.risk(c(5, 3, 1), c(0.1, 0.1, 0.8))# examples for adj.risk adj.risk(c(5, 1), c(0.1, 0.9)) adj.risk(c(5, 3, 1), c(0.1, 0.1, 0.8))
Calculates adjusted risk for given relative risk and population proportions. This is an intermediate calculation in the calculation of effective probability of infection for risk-based surveillance activities. This function is similar to Adj.risk, except is adapted for use with multiple simulations instead of single RR values.
adj.risk.sim(rr, ppr)adj.risk.sim(rr, ppr)
rr |
relative risk values (matrix of values, columns corresponding to the number of risk strata, rows corresponding to number of iterations for simulation ) |
ppr |
population proportions corresponding to rr values (vector of equal length to columns in rr) |
matrix of adjusted risk values (in order corresponding to rr)
# examples for adj.risk.sim its<- 10 risk.cat<- 3 rr<- matrix(0, nrow=its, ncol=risk.cat) rr[,1]<- mc2d::rpert(its, 5,10,20) rr[,2]<- mc2d::rpert(its, 2,3,5) rr[,3]<- 1 ppr<- c(0.05, 0.2, 0.75) adj.risk.sim(rr, ppr) adj.risk.sim(matrix(c(5, 3, 1), nrow=1), matrix(c(0.1, 0.1, 0.8), nrow=1))# examples for adj.risk.sim its<- 10 risk.cat<- 3 rr<- matrix(0, nrow=its, ncol=risk.cat) rr[,1]<- mc2d::rpert(its, 5,10,20) rr[,2]<- mc2d::rpert(its, 2,3,5) rr[,3]<- 1 ppr<- c(0.05, 0.2, 0.75) adj.risk.sim(rr, ppr) adj.risk.sim(matrix(c(5, 3, 1), nrow=1), matrix(c(0.1, 0.1, 0.8), nrow=1))
Estimates apparent prevalence and confidence limits for given sample size and result, assuming representative sampling
ap(x, n, type = "wilson", conf = 0.95)ap(x, n, type = "wilson", conf = 0.95)
x |
number of positives in sample |
n |
sample size, note: either x or n can be a vector, but at least one must be scalar |
type |
method for estimating CI, one of c("normal", "exact", "wilson", "jeffreys", "agresti-coull", "all"), default = "wilson" |
conf |
level of confidence required, default = 0.95 (scalar) |
either 1) if type = "all", a list with 5 elements, each element a matrix with 6 columns, x, n, proportion, lower confidence limit, upper confidence limit, confidence level and CI method; or 2) a matrix of results for the chosen method
# examples for ap function n<- 200 x<- 25 conf<- 0.95 ap(x, n) ap(seq(10, 100, 10), 200, type = "agresti") ap(seq(10, 100, 10), 200, type = "all")# examples for ap function n<- 200 x<- 25 conf<- 0.95 ap(x, n) ap(seq(10, 100, 10), 200, type = "agresti") ap(seq(10, 100, 10), 200, type = "all")
Calculates Agresti-Coull confidence limits for a simple proportion (apparent prevalence)
binom.agresti(x, n, conf = 0.95)binom.agresti(x, n, conf = 0.95)
x |
number of positives in sample |
n |
sample size, note: either x or n can be a vector, but at least one must be scalar |
conf |
level of confidence required, default 0.95 (scalar) |
a dataframe with 6 columns, x, n, proportion, lower confidence limit, upper confidence limit, confidence level and CI method
# test binom.agresti binom.agresti(25, 200) binom.agresti(seq(10, 100, 10), 200) binom.agresti(50, seq(100, 1000, 100))# test binom.agresti binom.agresti(25, 200) binom.agresti(seq(10, 100, 10), 200) binom.agresti(50, seq(100, 1000, 100))
Calculates Clopper-Pearson exact binomial confidence limits for a simple proportion (apparent prevalence)
binom.cp(x, n, conf = 0.95)binom.cp(x, n, conf = 0.95)
x |
number of positives in sample |
n |
sample size, note: either x or n can be a vector, but at least one must be scalar |
conf |
level of confidence required, default = 0.95 (scalar) |
a dataframe with 6 columns, x, n, proportion, lower confidence limit, upper confidence limit, confidence level and CI method
# test binom.cp binom.cp(25, 200) binom.cp(seq(10, 100, 10), 200) binom.cp(50, seq(100, 1000, 100))# test binom.cp binom.cp(25, 200) binom.cp(seq(10, 100, 10), 200) binom.cp(50, seq(100, 1000, 100))
Calculates Jeffreys confidence limits for a simple proportion (apparent prevalence)
binom.jeffreys(x, n, conf = 0.95)binom.jeffreys(x, n, conf = 0.95)
x |
number of positives in sample |
n |
sample size, note: either x or n can be a vector, but at least one must be scalar |
conf |
level of confidence required, default = 0.95 (scalar) |
a dataframe with 6 columns, x, n, proportion, lower confidence limit, upper confidence limit, confidence level and CI method
# test binom.jeffreys binom.jeffreys(25, 200) binom.jeffreys(seq(10, 100, 10), 200) binom.jeffreys(50, seq(100, 1000, 100))# test binom.jeffreys binom.jeffreys(25, 200) binom.jeffreys(seq(10, 100, 10), 200) binom.jeffreys(50, seq(100, 1000, 100))
Calculates the discounted prior probability of disease freedom, after adjusting for the probability of disease exceeding the design prevalence during the time period of the surveillance data being analysed
disc.prior(prior, p.intro)disc.prior(prior, p.intro)
prior |
prior probability of freedom before surveillance |
p.intro |
probability of introduction (or of prevalence exceeding the design prevalence) during the time period (scalar or vector equal length to prior) |
vector of discounted prior probabilities of freedom
# examples for disc.prior disc.prior(0.5, 0.01) disc.prior(0.95, c(0.001, 0.005, 0.01, 0.02, 0.05)) disc.prior(c(0.5, 0.6, 0.7, 0.8, 0.9, 0.95), 0.01)# examples for disc.prior disc.prior(0.5, 0.01) disc.prior(0.95, c(0.001, 0.005, 0.01, 0.02, 0.05)) disc.prior(c(0.5, 0.6, 0.7, 0.8, 0.9, 0.95), 0.01)
Calculates effective probability of infection (adjusted design prevalence) for each risk group for risk-based surveillance activities
epi.calc(pstar, rr, ppr)epi.calc(pstar, rr, ppr)
pstar |
design prevalence (scalar) |
rr |
relative risk values (vector of values corresponding to the number of risk strata) |
ppr |
population proportions corresponding to rr values (vector of equal length to rr) |
list of 2 elements, a vector of EPI values and a vector of corresponding adjusted risks (in corresponding order to rr)
# examples for epi.calc epi.calc(0.1, c(5, 1), c(0.1, 0.9)) epi.calc(0.02, c(5, 3, 1), c(0.1, 0.1, 0.8))# examples for epi.calc epi.calc(0.1, c(5, 1), c(0.1, 0.9)) epi.calc(0.02, c(5, 3, 1), c(0.1, 0.1, 0.8))
Calculates sample sizes for a 2-stage representative survey (sampling of clusters and units within clusters) for disease freedom or detection, assuming imperfect test sensitivity, perfect test specificity and representative sampling
n.2stage(H = NA, N = NA, sep.sys = 0.95, sep.c, pstar.c, pstar.u, se = 1)n.2stage(H = NA, N = NA, sep.sys = 0.95, sep.c, pstar.c, pstar.u, se = 1)
H |
population size = number of clusters or NA if not known, default = NA |
N |
population sizes for clusters, default = NA, scalar or vector of population sizes for clusters |
sep.sys |
desired population sensitivity (scalar) |
sep.c |
desired cluster-level sensitivity (scalar) |
pstar.c |
specified cluster-level design prevalence as proportion or integer (scalar) |
pstar.u |
specified population-level design prevalence as proportion or integer (scalar) |
se |
unit sensitivity (scalar) |
a list of 2 elements, the number of clusters to sample and a vector of sample sizes per cluster
# examples of n.2stage - checked n.2stage(NA, NA, 0.95, 0.5, 0.01, 0.1, 0.95) n.2stage(500, NA, 0.95, 0.5, 10, 0.1, 0.95) n.2stage(1000, c(50, 100, 200, 500, 1000, 5000, NA), 0.95, 0.5, 0.01, 0.05, 0.8) n.2stage(1000, c(50, 100, 200, 500, 1000, 5000, NA), 0.95, 0.5, 0.01, 1, 0.8) n.2stage(1000, c(50, 100, 200, 500, 1000, 5000, NA), 0.9, 0.95, 1, 0.1, 0.8)# examples of n.2stage - checked n.2stage(NA, NA, 0.95, 0.5, 0.01, 0.1, 0.95) n.2stage(500, NA, 0.95, 0.5, 10, 0.1, 0.95) n.2stage(1000, c(50, 100, 200, 500, 1000, 5000, NA), 0.95, 0.5, 0.01, 0.05, 0.8) n.2stage(1000, c(50, 100, 200, 500, 1000, 5000, NA), 0.95, 0.5, 0.01, 1, 0.8) n.2stage(1000, c(50, 100, 200, 500, 1000, 5000, NA), 0.9, 0.95, 1, 0.1, 0.8)
Calculates sample size for estimating apparent prevalence (simple proportion)
n.ap(p, precision, conf = 0.95)n.ap(p, precision, conf = 0.95)
p |
expected proportion, scalar or vector of values |
precision |
absolute precision, +/- proportion equivalent to half the width of the desired confidence interval, scalar or vector of values, note: at least one of p and precision must be a scalar |
conf |
level of confidence required, default = 0.95 (scalar) |
a vector of sample sizes
# examples of n.ap n.ap(0.5, 0.1) n.ap(0.5, 0.1, conf=0.99) n.ap(seq(0.1, 0.5, by = 0.1), 0.05) n.ap(0.2, c(0.01, 0.02, 0.05, 0.1))# examples of n.ap n.ap(0.5, 0.1) n.ap(0.5, 0.1, conf=0.99) n.ap(seq(0.1, 0.5, by = 0.1), 0.05) n.ap(0.2, c(0.01, 0.02, 0.05, 0.1))
Calculates sample size for demonstrating freedom or detecting disease using binomial approach and assuming imperfect test sensitivity, perfect test specificity and representative sampling
n.binom(sep, pstar, se = 1)n.binom(sep, pstar, se = 1)
sep |
desired population sensitivity (scalar or vector) |
pstar |
specified design prevalence (scalar or vector of same length as sep) |
se |
unit sensitivity, default = 1 (scalar or vector of same length as sep) |
vector of sample sizes
# examples for n.binom - checked n.binom(sep=0.95, pstar=c(0.01, 0.02, 0.05, 0.1, 0.2)) n.binom(c(0.5, 0.8, 0.9, 0.95), 0.01)# examples for n.binom - checked n.binom(sep=0.95, pstar=c(0.01, 0.02, 0.05, 0.1, 0.2)) n.binom(c(0.5, 0.8, 0.9, 0.95), 0.01)
Calculates optimum sample size and cut-point number of positives to achieve specified population sensitivity, for given population size and other parameters, using freecalc algorithm, all paramaters must be scalars
n.c.freecalc(N, sep = 0.95, c = 1, se, sp = 1, pstar, minSpH = 0.95)n.c.freecalc(N, sep = 0.95, c = 1, se, sp = 1, pstar, minSpH = 0.95)
N |
population size |
sep |
target population sensitivity |
c |
The maximum allowed cut-point number of positives to classify a cluster as positive, default=1, if positives < c result is negative, >= c is positive |
se |
test unit sensitivity |
sp |
test unit specificity, default=1 |
pstar |
design prevalence as a proportion or integer (number of infected units) |
minSpH |
minimium desired population specificity |
a list of 3 elements, a dataframe with 1 row and six columns for the recommended sample size and corresponding values for population sensitivity (SeP), population specificity (SpP), N, c and pstar, a vector of SeP values and a vector of SpP values, for n = 1:N
# examples for n.c.hp n.c.freecalc(120,0.95,c=5,se=0.9,sp=0.99,pstar=0.1, minSpH=0.9)[[1]] n.c.freecalc(65,0.95,c=5,se=0.95,sp=0.99,pstar=0.05, minSpH=0.9)# examples for n.c.hp n.c.freecalc(120,0.95,c=5,se=0.9,sp=0.99,pstar=0.1, minSpH=0.9)[[1]] n.c.freecalc(65,0.95,c=5,se=0.95,sp=0.99,pstar=0.05, minSpH=0.9)
Calculates optimum sample size and cut-point positives to achieve specified population sensitivity, for given population size and other parameters, all paramaters must be scalars
n.c.hp(N, sep = 0.95, c = 1, se, sp = 1, pstar, minSpH = 0.95)n.c.hp(N, sep = 0.95, c = 1, se, sp = 1, pstar, minSpH = 0.95)
N |
population size |
sep |
target population sensitivity |
c |
The maximum allowed cut-point number of positives to classify a cluster as positive, default=1, if positives < c result is negative, >= c is positive |
se |
test unit sensitivity |
sp |
test unit specificity, default=1 |
pstar |
design prevalence as a proportion or integer (number of infected units) |
minSpH |
minimium desired population specificity |
a list of 3 elements, a dataframe with 1 row and six columns for the recommended sample size and corresponding values for population sensitivity (SeP), population specificity (SpP), N, c and pstar, a vector of SeP values and a vector of SpP values, for n = 1:N
# examples for n.c.hp n.c.hp(65,0.95,c=5,se=0.95,sp=0.99,pstar=0.05, minSpH=0.9)[[1]] tmp<- n.c.hp(120,0.95,c=5,se=0.9,sp=0.99,pstar=0.1, minSpH=0.9)# examples for n.c.hp n.c.hp(65,0.95,c=5,se=0.95,sp=0.99,pstar=0.05, minSpH=0.9)[[1]] tmp<- n.c.hp(120,0.95,c=5,se=0.9,sp=0.99,pstar=0.1, minSpH=0.9)
Calculates sample size required for a specified population sensitivity, for a given population size, cut-point number of positives and other parameters, using Freecalc algorithm. All paramaters must be scalars
n.freecalc(N, sep = 0.95, c = 1, se, sp = 1, pstar, minSpH = 0.95)n.freecalc(N, sep = 0.95, c = 1, se, sp = 1, pstar, minSpH = 0.95)
N |
population size |
sep |
target population sensitivity |
c |
The cut-point number of positives to classify a cluster as positive, default=1, if positives < c result is negative, >= c is positive |
se |
test unit sensitivity |
sp |
test unit specificity, default=1 |
pstar |
design prevalence as a proportion or integer (number of infected units) |
minSpH |
minimium desired population specificity |
a list of 2 elements, a dataframe with 1 row and six columns for the recommended sample size and corresponding values for population sensitivity (SeP), population specificity (SpP), N, c and pstar and a dataframe of n rows with SeP and SpP values for each value of n up to the recommended value
# examples for n.freecalc n.freecalc(65,0.95,c=1,se=0.95,sp=0.99,pstar=0.05, minSpH=0.9)[[1]] n.freecalc(65,0.95,c=2,se=0.95,sp=0.99,pstar=0.05, minSpH=0.9)[[1]] n.freecalc(65,0.95,c=3,se=0.95,sp=0.99,pstar=0.05, minSpH=0.9)# examples for n.freecalc n.freecalc(65,0.95,c=1,se=0.95,sp=0.99,pstar=0.05, minSpH=0.9)[[1]] n.freecalc(65,0.95,c=2,se=0.95,sp=0.99,pstar=0.05, minSpH=0.9)[[1]] n.freecalc(65,0.95,c=3,se=0.95,sp=0.99,pstar=0.05, minSpH=0.9)
Calculates sample size for demonstrating freedom or detecting disease using the appropriate method, depending on whether or not N provided (hypergeometric if N provided, binomial otherwise), assuming imperfect test sensitivity, perfect test specificity and representative sampling
n.freedom(N = NA, sep = 0.95, pstar, se = 1)n.freedom(N = NA, sep = 0.95, pstar, se = 1)
N |
population size, default = NA (unknown) (scalar or vector of same length as sep) |
sep |
desired population sensitivity (scalar or vector) |
pstar |
specified design prevalence as proportion or integer (scalar or vector of same length as sep) |
se |
unit sensitivity (scalar or vector of same length as sep) |
vector of sample sizes, NA if N is specified and n>N
# examples for n.freedom - checked n.freedom(NA, sep=0.95, pstar=0.01, se=1) n.freedom(500, sep=0.95, pstar=0.01, se=1) n.freedom(N=c(100, 500, 1000, 5000, 10000, 100000, NA), sep=0.95, pstar=0.01, se=1) n.freedom(500, sep=0.95, pstar=0.01, se=c(0.5, 0.6, 0.7, 0.8, 0.9, 0.99, 1))# examples for n.freedom - checked n.freedom(NA, sep=0.95, pstar=0.01, se=1) n.freedom(500, sep=0.95, pstar=0.01, se=1) n.freedom(N=c(100, 500, 1000, 5000, 10000, 100000, NA), sep=0.95, pstar=0.01, se=1) n.freedom(500, sep=0.95, pstar=0.01, se=c(0.5, 0.6, 0.7, 0.8, 0.9, 0.99, 1))
Calculates sample size to achieve specified population sensitivity with population specificity >= specified minimum value, for given population size, cut-point number of positives and other parameters, all paramaters must be scalars
n.hp(N, sep = 0.95, c = 1, se, sp = 1, pstar, minSpH = 0.95)n.hp(N, sep = 0.95, c = 1, se, sp = 1, pstar, minSpH = 0.95)
N |
population size |
sep |
target population sensitivity |
c |
The cut-point number of positives to classify a cluster as positive, default=1, if positives < c result is negative, >= c is positive |
se |
test unit sensitivity |
sp |
test unit specificity, default=1 |
pstar |
design prevalence as a proportion or integer (number of infected units) |
minSpH |
minimium desired population specificity |
A list of 2 elements, a dataframe with 1 row and six columns for the recommended sample size and corresponding values for population sensitivity (SeP), population specificity (SpP), N, c and pstar and a dataframe of n rows with SeP and SpP values for each value of n up to the recommended value. Returns sample size for maximum achievable sep if it is not possible to achieve target sep AND SpP>= minSpH.
# examples for n.hp n.hp(65,0.95,c=1,se=0.95,sp=0.99,pstar=0.05, minSpH=0.9)[[1]] n.hp(65,0.95,c=2,se=0.95,sp=0.99,pstar=0.05, minSpH=0.9)# examples for n.hp n.hp(65,0.95,c=1,se=0.95,sp=0.99,pstar=0.05, minSpH=0.9)[[1]] n.hp(65,0.95,c=2,se=0.95,sp=0.99,pstar=0.05, minSpH=0.9)
Calculates sample size for demonstrating freedom or detecting disease using hypergeometric approximation and assuming imperfect test sensitivity, perfect test specificity and representative sampling
n.hypergeo(sep, N, d, se = 1)n.hypergeo(sep, N, d, se = 1)
sep |
desired population sensitivity (scalar or vector) |
N |
population size (scalar or vector of same length as sep) |
d |
expected number of infected units in population, = design prevalence*N rounded to next integer (scalar or vector of same length as sep) |
se |
unit sensitivity, default = 1 (scalar or vector of same length as sep) |
vector of sample sizes, NA if n>N
# examples for n.hypergeo - checked n.hypergeo(0.95, N=100, d=1, se = 0.95) n.hypergeo(sep=0.95, N=c(100, 200, 500, 1000, 10000), d=ceiling(0.01*c(100, 200, 500, 1000, 10000))) n.hypergeo(c(0.5, 0.8, 0.9, 0.95), N=100, d=5) n.hypergeo(0.95, N=80, d=c(1, 2, 5, 10)) n.hypergeo(0.95, N=80, d=c(1, 2, 5, 10), se = 0.8)# examples for n.hypergeo - checked n.hypergeo(0.95, N=100, d=1, se = 0.95) n.hypergeo(sep=0.95, N=c(100, 200, 500, 1000, 10000), d=ceiling(0.01*c(100, 200, 500, 1000, 10000))) n.hypergeo(c(0.5, 0.8, 0.9, 0.95), N=100, d=5) n.hypergeo(0.95, N=80, d=c(1, 2, 5, 10)) n.hypergeo(0.95, N=80, d=c(1, 2, 5, 10), se = 0.8)
Calculates the sample size required to achieve a given value for probability of disease freedom
n.pfree(pfree, prior, p.intro, pstar, se, N = NA)n.pfree(pfree, prior, p.intro, pstar, se, N = NA)
pfree |
desired probability of freedom (scalar or vector) |
prior |
prior probability of freedom before surveillance (scalar or vector of same length as pfree) |
p.intro |
probability of introduction for time period (scalar or vector of same length as pfree) |
pstar |
design prevalence (scalar or vector of same length as pfree) |
se |
unit sensitivity (scalar or vector of same length as pfree) |
N |
population size (scalar or vector of same length as pfree) |
a list of 3 elements, the first a vector of sample sizes and the second a corresponding vector of population sensitivity values and the third a vector of adjusted priors
# examples for n.pfree n.pfree(0.95, 0.5, 0.01, 0.05, 0.9) n.pfree(0.95, 0.5, 0.01, 0.05, 0.9, N=300) n.pfree(pfree = c(0.9, 0.95, 0.98, 0.99), prior = 0.7, 0.01, 0.01, 0.8, 1000) n.pfree(0.95, 0.7, 0.01, 0.1, 0.96)# examples for n.pfree n.pfree(0.95, 0.5, 0.01, 0.05, 0.9) n.pfree(0.95, 0.5, 0.01, 0.05, 0.9, N=300) n.pfree(pfree = c(0.9, 0.95, 0.98, 0.99), prior = 0.7, 0.01, 0.01, 0.8, 1000) n.pfree(0.95, 0.7, 0.01, 0.1, 0.96)
Calculates sample size to achieve desired population-level sensitivity, assuming pooled sampling and allowing for imperfect sensitivity and specificity of the pooled test
n.pooled(sep, k, pstar, pse, psp = 1)n.pooled(sep, k, pstar, pse, psp = 1)
sep |
desired population sensitivity (scalar or vector) |
k |
pool size (constant across pools) (scalar or vector of same length as sep) |
pstar |
design prevalence (scalar or vector of same length as sep) |
pse |
pool-level sensitivity (scalar or vector of same length as sep) |
psp |
pool-level specificity (scalar or vector of same length as sep) |
vector of sample sizes
# examples for n.pooled n.pooled(0.95, 5, 0.01, 1, 1) n.pooled(0.95, 10, 0.1, 0.9, 1) n.pooled(0.95, c(2, 5, 10, 20), 0.1, c(0.99, 0.98, 0.97, 0.95), 1)# examples for n.pooled n.pooled(0.95, 5, 0.01, 1, 1) n.pooled(0.95, 10, 0.1, 0.9, 1) n.pooled(0.95, c(2, 5, 10, 20), 0.1, c(0.99, 0.98, 0.97, 0.95), 1)
Calculates sample size for risk-based sampling for a single risk factor and using binomial method
n.rb(pstar, rr, ppr, spr, se, sep)n.rb(pstar, rr, ppr, spr, se, sep)
pstar |
design prevalence (scalar) |
rr |
relative risk values (vector, length equal to the number of risk strata) |
ppr |
population proportions corresponding to rr values (vector of equal length to rr) |
spr |
planned surveillance proportion for each risk group (vector equal length to rr, ppr) |
se |
unit sensitivity (fixed or vector same length as rr, ppr, n) |
sep |
required population sensitivity (scalar) |
list of 2 elements, a vector of sample sizes for each risk group a scalar of total sample size, a vector of EPI values and a vector of adjusted risks
# examples for n.rb n.rb(0.1, c(5, 3, 1), c(0.1, 0.10, 0.80), c(0.5, 0.3, 0.2), 0.9, 0.95) n.rb(0.01, c(5, 1), c(0.1, 0.9), c(0.8, 0.2), c(0.9, 0.95), 0.95)# examples for n.rb n.rb(0.1, c(5, 3, 1), c(0.1, 0.10, 0.80), c(0.5, 0.3, 0.2), 0.9, 0.95) n.rb(0.01, c(5, 1), c(0.1, 0.9), c(0.8, 0.2), c(0.9, 0.95), 0.95)
Calculates sample size required (clusters and units) for a 2-stage risk-based survey with a single risk factor at the cluster level only.
n.rb.2stage.1(rr, ppr, spr, pstar.c, pstar.u, se = 1, sep.c = 0.95, sep.sys = 0.95)n.rb.2stage.1(rr, ppr, spr, pstar.c, pstar.u, se = 1, sep.c = 0.95, sep.sys = 0.95)
rr |
relative risk values (vector of values, corresponding to the number of risk strata) |
ppr |
population proportions corresponding to rr values (vector of equal length to rr) |
spr |
planned surveillance proportions corresponding to rr values - the proportions of the total sample to be collected from each risk stratum (vector of equal length to rr). |
pstar.c |
cluster (herd) level design prevalence, scalar, either proportion or integer |
pstar.u |
unit (animal) level design prevalence, scalar, either proportion or integer |
se |
unit sensitivity of test (proportion), scalar, default = 1 |
sep.c |
desired cluster-level sensitivity (proportion), scalar, default = 0.95 |
sep.sys |
desired population-level sensitivity (proportion), scalar, default = 0.95 |
A list of seven elements: 1) a vector (of the same length as rr) of the numbers of clusters to sample from each risk stratum, 2) the total number of clusters to be sampled, 3) a vector of EPI values for each risk stratum, 4) a vector of adjusted risk values for each risk stratum, 5) the number of untis to be sampled per cluster 6) a vector of the total numbers of units to be sampled for each risk stratum 7) the overall total number of units to be sampled
rr<- c(5,3,1) ppr<- c(0.1, 0.2, 0.7) spr<- c(0.4, 0.4, 0.2) n.rb.2stage.1(rr, ppr, spr, pstar.c=0.01, pstar.u=0.1, se =0.9, sep.c=0.8, sep.sys=0.95) n.rb.2stage.1(c(3,1), c(0.2,0.8), c(0.7,0.3),0.05, 0.1, 0.9, 0.95, 0.99)rr<- c(5,3,1) ppr<- c(0.1, 0.2, 0.7) spr<- c(0.4, 0.4, 0.2) n.rb.2stage.1(rr, ppr, spr, pstar.c=0.01, pstar.u=0.1, se =0.9, sep.c=0.8, sep.sys=0.95) n.rb.2stage.1(c(3,1), c(0.2,0.8), c(0.7,0.3),0.05, 0.1, 0.9, 0.95, 0.99)
Calculates sample size required (clusters and units) for a 2-stage risk-based survey with risk factors at either cluster level or unit level, or both.
n.rb.2stage.2(rr.c, ppr.c, spr.c, pstar.c, rr.u = 1, ppr.u = 1, spr.u = 1, pstar.u, se = 1, sep.c = 0.95, sep.sys = 0.95)n.rb.2stage.2(rr.c, ppr.c, spr.c, pstar.c, rr.u = 1, ppr.u = 1, spr.u = 1, pstar.u, se = 1, sep.c = 0.95, sep.sys = 0.95)
rr.c |
relative risk values at the cluster level (vector of values, corresponding to the number of risk strata) |
ppr.c |
population proportions at the cluster level, corresponding to rr.c values (vector of equal length to rr.c) |
spr.c |
planned surveillance proportions at the cluster level, corresponding to rr.c values - the proportions of the total sample to be collected from each risk stratum (vector of equal length to rr.c). |
pstar.c |
cluster (herd) level design prevalence, scalar, either proportion or integer |
rr.u |
relative risk values at the unit level (vector of values, corresponding to the number of risk strata) |
ppr.u |
population proportions at the unit level, corresponding to rr.u values (vector of equal length to rr.u) |
spr.u |
planned surveillance proportions at the unit level, corresponding to rr.u values - the proportions of the total sample to be collected from each risk stratum (vector of equal length to rr.u). |
pstar.u |
unit (animal) level design prevalence, scalar, either proportion or integer |
se |
unit sensitivity of test (proportion), scalar, default = 1 |
sep.c |
desired cluster-level sensitivity (proportion), scalar, default = 0.95 |
sep.sys |
desired population-level sensitivity (proportion), scalar, default = 0.95 |
A list of cluster and unit level results number of clusters/units to sample per risk stratum, the total number of clusters or units per cluster to be sampled and vectors of EPI and adjusted risk values for each risk stratum.
rr.c<- c(5,3,1) ppr.c<- c(0.1, 0.2, 0.7) spr.c<- c(0.4, 0.4, 0.2) rr.u<- c(4,1) ppr.u<- c(0.1, 0.9) spr.u<- c(1, 0) n.rb.2stage.2(rr.c, ppr.c, spr.c, pstar.c=0.02, rr.u, ppr.u, spr.u, 0.1, se=0.9, sep.c=0.5, sep.sys=0.95) n.rb.2stage.2(c(3,1), c(0.2,0.8), c(0.7,0.3), pstar.c=0.05, pstar.u=0.1, se=0.9, sep.c=0.95, sep.sys=0.99)rr.c<- c(5,3,1) ppr.c<- c(0.1, 0.2, 0.7) spr.c<- c(0.4, 0.4, 0.2) rr.u<- c(4,1) ppr.u<- c(0.1, 0.9) spr.u<- c(1, 0) n.rb.2stage.2(rr.c, ppr.c, spr.c, pstar.c=0.02, rr.u, ppr.u, spr.u, 0.1, se=0.9, sep.c=0.5, sep.sys=0.95) n.rb.2stage.2(c(3,1), c(0.2,0.8), c(0.7,0.3), pstar.c=0.05, pstar.u=0.1, se=0.9, sep.c=0.95, sep.sys=0.99)
Calculates sample size for risk-based sampling for a single risk factor and varying unit sensitivity, using binomial method
n.rb.varse(pstar, rr, ppr, spr, se, spr.rg, sep)n.rb.varse(pstar, rr, ppr, spr, se, spr.rg, sep)
pstar |
design prevalence (scalar) |
rr |
relative risk values (vector, length equal to the number of risk strata) |
ppr |
population proportions for each risk group, vector of same length as rr |
spr |
planned surveillance proportions for each risk group, vector of same length as rr |
se |
unit sensitivities (vector of group values) |
spr.rg |
proportions of samples for each sensitivity value in each risk group (matrix with rows = risk groups, columns = sensitivity values), row sums must equal 1 |
sep |
required population sensitivity (scalar) |
list of 3 elements, a matrix of sample sizes for each risk and sensitivity group, a vector of EPI values and a vector of mean sensitivity for each risk group
# examples for n.rb.varse m<- rbind(c(0.8, 0.2), c(0.5, 0.5), c(0.7, 0.3)) n.rb.varse(0.01, c(5, 3, 1), c(0.1, 0.1, 0.8), c(0.4, 0.4, 0.2), c(0.92, 0.8), m, 0.95) m<- rbind(c(0.8, 0.2), c(0.6, 0.4)) n.rb.varse(0.05, c(3, 1), c(0.2, 0.8), c(0.7, 0.3), c(0.95, 0.8), m, 0.95) m<- rbind(c(1), c(1)) n.rb.varse(0.05, c(3, 1), c(0.2, 0.8), c(0.7, 0.3), c(0.95), m, 0.99)# examples for n.rb.varse m<- rbind(c(0.8, 0.2), c(0.5, 0.5), c(0.7, 0.3)) n.rb.varse(0.01, c(5, 3, 1), c(0.1, 0.1, 0.8), c(0.4, 0.4, 0.2), c(0.92, 0.8), m, 0.95) m<- rbind(c(0.8, 0.2), c(0.6, 0.4)) n.rb.varse(0.05, c(3, 1), c(0.2, 0.8), c(0.7, 0.3), c(0.95, 0.8), m, 0.95) m<- rbind(c(1), c(1)) n.rb.varse(0.05, c(3, 1), c(0.2, 0.8), c(0.7, 0.3), c(0.95), m, 0.99)
Calculates sample size for estimating true prevalence using normal approximation
n.tp(p, se, sp, precision, conf = 0.95)n.tp(p, se, sp, precision, conf = 0.95)
p |
estimated true prevalence (scalar or vector) |
se |
test sensitivity (scalar or vector) |
sp |
test specificity (scalar or vector) |
precision |
absolute precision, +/- proportion equal to half the width of the desired confidence interval (scalar or vector) |
conf |
desired level of confidence for CI, default = 0.95 (scalar or vector) |
a vector of sample sizes
# examples for n.tp n.tp(0.1, 0.9, 0.99, 0.05) n.tp(0.1, 0.9, 0.99, 0.05, conf = 0.99) n.tp(c(0.05, 0.1, 0.2, 0.3, 0.4, 0.5), 0.9, 0.99, 0.05) n.tp(0.5, 0.9, 0.99, c(0.01, 0.02, 0.05, 0.1, 0.2))# examples for n.tp n.tp(0.1, 0.9, 0.99, 0.05) n.tp(0.1, 0.9, 0.99, 0.05, conf = 0.99) n.tp(c(0.05, 0.1, 0.2, 0.3, 0.4, 0.5), 0.9, 0.99, 0.05) n.tp(0.5, 0.9, 0.99, c(0.01, 0.02, 0.05, 0.1, 0.2))
Calculates the posterior probability (confidence) of disease freedom (negative predictive value) for a single time period
pfree.1(sep, p.intro, prior = 0.5)pfree.1(sep, p.intro, prior = 0.5)
sep |
population sensitivity for time period (scalar or vector) |
p.intro |
probability of introduction for time period (scalar or vector of same length as sep) |
prior |
prior probability of freedom before surveillance (scalar or vector of same length as sep) |
data.frame with columns for sep, p.intro, discounted
prior, pfree, pfree.equ and prior.equ
# examples for pfree.1 pfree.1(0.8, 0.01, 0.5) pfree.1(0.6, c(0.001, 0.005, 0.01, 0.02, 0.05), 0.5) pfree.1(runif(10, 0.4, 0.6), 0.01, 0.5) pfree.1(runif(10, 0.4, 0.6), runif(10, 0.005, 0.015), 0.5)# examples for pfree.1 pfree.1(0.8, 0.01, 0.5) pfree.1(0.6, c(0.001, 0.005, 0.01, 0.02, 0.05), 0.5) pfree.1(runif(10, 0.4, 0.6), 0.01, 0.5) pfree.1(runif(10, 0.4, 0.6), runif(10, 0.005, 0.015), 0.5)
Calculates the probability (confidence) of disease freedom for given prior, sep and p.intro over 1 or more time periods
pfree.calc(sep, p.intro, prior = 0.5, discount.1 = TRUE)pfree.calc(sep, p.intro, prior = 0.5, discount.1 = TRUE)
sep |
population sensitivity for each time period (vector) |
p.intro |
probability of introduction for each time period (scalar or vector of same length as sep) |
prior |
prior probability of freedom before surveillance (scalar) |
discount.1 |
logical variable whether or not to discount first time period. |
data.frame with columns for sep, p.intro, discounted
prior, probability of freedom, equilibrium probability of freedom
and equilibrium prior
# examples for pfree.calc pfree.calc(0.8, 0.01, 0.5) pfree.calc(0.8, 0.01, 0.5, FALSE) pfree.calc(rep(0.6,24), 0.01, 0.5) pfree.calc(rep(0.6,24), 0.01, 0.5, FALSE) pfree.calc(runif(10, 0.4, 0.6), 0.01, 0.5) pfree.calc(runif(10, 0.4, 0.6), runif(10, 0.005, 0.015), 0.5)# examples for pfree.calc pfree.calc(0.8, 0.01, 0.5) pfree.calc(0.8, 0.01, 0.5, FALSE) pfree.calc(rep(0.6,24), 0.01, 0.5) pfree.calc(rep(0.6,24), 0.01, 0.5, FALSE) pfree.calc(runif(10, 0.4, 0.6), 0.01, 0.5) pfree.calc(runif(10, 0.4, 0.6), runif(10, 0.005, 0.015), 0.5)
Calculates equilibrium probability of disease freedom and equilibrium prior probability of freedom, after discounting for probability of introduction
pfree.equ(sep, p.intro)pfree.equ(sep, p.intro)
sep |
population sensitivity for time period (scalar or vector) |
p.intro |
probability of introduction for time period (scalar or vector of same length as sep) |
a list of 2 vectors, equilibrium posterior probability of freedom and equilibrium prior (discounted) probability of freedom
# examples of pfree.equ pfree.equ(runif(10, 0.4, 0.6), 0.01) pfree.equ(0.8, 0.05) pfree.equ(rep(0.9, 6), c(0.0001, 0.0005, 0.001, 0.005, 0.01, 0.05))# examples of pfree.equ pfree.equ(runif(10, 0.4, 0.6), 0.01) pfree.equ(0.8, 0.05) pfree.equ(rep(0.9, 6), c(0.0001, 0.0005, 0.001, 0.005, 0.01, 0.05))
Calculates design prevalence required for given sample size and desired population-level sensitivity, assuming imperfect test sensitivity, perfect test specificity and representative sampling
pstar.calc(N = NA, n, sep, se)pstar.calc(N = NA, n, sep, se)
N |
populaton size if known (scalar or vector of same length as n) |
n |
sample size (scalar or vector) |
sep |
desired population sensitivity (scalar or vector of same length as n) |
se |
unit sensitivity (scalar or vector of same length as n) |
vector of design prevalence values
# examples of pstar.calc- checked pstar.calc(NA, 280, 0.95, 0.98) pstar.calc(500, 250, sep=0.95, se=1) pstar.calc(N=c(100, 500, 1000, 5000, 10000, 100000, NA), n=30, sep=0.95, se=1) pstar.calc(500, n=30, sep=0.95, se=c(0.5, 0.6, 0.7, 0.8, 0.9, 0.99, 1))# examples of pstar.calc- checked pstar.calc(NA, 280, 0.95, 0.98) pstar.calc(500, 250, sep=0.95, se=1) pstar.calc(N=c(100, 500, 1000, 5000, 10000, 100000, NA), n=30, sep=0.95, se=1) pstar.calc(500, n=30, sep=0.95, se=c(0.5, 0.6, 0.7, 0.8, 0.9, 0.99, 1))
Calculates the combined sensitivity for multiple tests interpreted in parallel (assuming independence)
se.parallel(se)se.parallel(se)
se |
vector of unit sensitivity values |
scalar of combined sensitivity, assuming independence
# examples for se.parallel se.parallel(c(0.99, 0.95, 0.8))# examples for se.parallel se.parallel(c(0.99, 0.95, 0.8))
Calculates the combined sensitivity for multiple tests interpreted in series (assuming independence)
se.series(se)se.series(se)
se |
vector of unit sensitivity values |
scalar of combined sensitivity, assuming independence
# examples for se.series se.series(c(0.99, 0.95, 0.8))# examples for se.series se.series(c(0.99, 0.95, 0.8))
Calculates population sensitivity using appropriate method, depending on whether or not N provided (hypergeometric if N provided, binomial otherwise), assuming perfect test specificity and representative sampling
sep(N = NA, n, pstar, se = 1, dig = 5)sep(N = NA, n, pstar, se = 1, dig = 5)
N |
population size, NA or vector of same length as n |
n |
sample size (number tested), scalar or vector |
pstar |
design prevalence as a proportion or integer, scalar or vector of same length as n |
se |
unit sensitivity, scalar or vector of same length as n |
dig |
number of digits for rounding of results |
a vector of population-level sensitivities
# examples for sep - checked sep(n=300, pstar=0.01, se=1) sep(NA, 300, 0.01, 1) sep(10000, 150, 0.02, 1) sep(n=1:100, pstar = 0.05, se=0.95) N<- seq(30, 100, by = 5) se<- 0.95 pstar<- 0.1 n<- rep(30, length(N)) sep(N, n, pstar, se = se) sep(rep(100, 10), seq(10, 100, by = 10), pstar = 1, se=0.99) N<- c(55, 134, NA, 44, 256) n<- c(15, 30, 28, 15, 33) sep(N, n, 0.1, 0.95)# examples for sep - checked sep(n=300, pstar=0.01, se=1) sep(NA, 300, 0.01, 1) sep(10000, 150, 0.02, 1) sep(n=1:100, pstar = 0.05, se=0.95) N<- seq(30, 100, by = 5) se<- 0.95 pstar<- 0.1 n<- rep(30, length(N)) sep(N, n, pstar, se = se) sep(rep(100, 10), seq(10, 100, by = 10), pstar = 1, se=0.99) N<- c(55, 134, NA, 44, 256) n<- c(15, 30, 28, 15, 33) sep(N, n, 0.1, 0.95)
Calculates population sensitivity for detecting disease, assuming imperfect test sensitivity and specificity and representative sampling, using binomial distribution (assumes large or unknown population size and that cut-point number of reactors for a positive result = 1)
sep.binom(n, pstar, se = 1, sp = 1, dig = 5)sep.binom(n, pstar, se = 1, sp = 1, dig = 5)
n |
sample size = number of units tested (integer), scalar or vector |
pstar |
design prevalence as a proportion (scalar or vector of same length as n) |
se |
unit sensitivity of test (proportion), default = 1 (scalar or vector of same length as n) |
sp |
unit specificity of test (proportion), default = 1 (scalar or vector of same length as n) |
dig |
number of digits for rounding of results |
vector of population-level sensitivities
# examples for sep.binom - checked sep.binom(n=300, pstar = 0.02, se = 0.92) tested<- seq(10,100, by=10) prev<- 0.05 sens<- 0.9 sep.binom(tested, prev, sens)# examples for sep.binom - checked sep.binom(n=300, pstar = 0.02, se = 0.92) tested<- seq(10,100, by=10) prev<- 0.05 sens<- 0.9 sep.binom(tested, prev, sens)
Calculates population sensitivity for a large or unknown population and allowing for imperfect test sensitivity and specificity, using Binomial distribution an allowing for a variable cut-point number of positives to classify as positive
sep.binom.imperfect(n, c = 1, se, sp = 1, pstar)sep.binom.imperfect(n, c = 1, se, sp = 1, pstar)
n |
sample size (scalar or vector) |
c |
The cut-point number of positives to classify a cluster as positive, default=1, if positives < c result is negative, >= c is positive (scalar or vector of same length as n) |
se |
test unit sensitivity (scalar or vector of same length as n) |
sp |
test unit specificity, default=1 (scalar or vector of same length as n) |
pstar |
design prevalence as a proportion (scalar or vector of same length as n) |
a vector of population-level sensitivities
# examples for sep.imperfect.binom sep.binom.imperfect(1:10*5, 2, 0.95, 0.98, 0.1) sep.binom.imperfect(50, 1:5, 0.95, 0.98, 0.1) sep.binom.imperfect(30, 2, 0.9, 0.98, 0.1) sep.binom.imperfect(30, 1, 0.9, 0.98, 0.1)# examples for sep.imperfect.binom sep.binom.imperfect(1:10*5, 2, 0.95, 0.98, 0.1) sep.binom.imperfect(50, 1:5, 0.95, 0.98, 0.1) sep.binom.imperfect(30, 2, 0.9, 0.98, 0.1) sep.binom.imperfect(30, 1, 0.9, 0.98, 0.1)
Calculates population sensitivity for detecting disease assuming imperfect test sensitivity, perfect test specificity and a census of all units in the population
sep.exact(d = 1, se = 1, dig = 5)sep.exact(d = 1, se = 1, dig = 5)
d |
expected number of infected units in population (=design prevalence*N rounded to next integer), scalar or vector of same length as se |
se |
unit sensitivity of test (proportion), scalar or vector |
dig |
number of digits for rounding of results |
vector of population-level sensitivities
# examples for sep.exact - checked sep.exact(d=1, se = 0.92) inf<- 1:5 sens<- 0.8 sep.exact(d=inf, se=sens) sep.exact(se=0.8, d = ceiling(0.01*c(10, 50, 100, 250, 500)))# examples for sep.exact - checked sep.exact(d=1, se = 0.92) inf<- 1:5 sens<- 0.8 sep.exact(d=inf, se=sens) sep.exact(se=0.8, d = ceiling(0.01*c(10, 50, 100, 250, 500)))
Calculates population sensitivity for a finite population and allowing for imperfect test sensitivity and specificity, using Freecalc method
sep.freecalc(N, n, c = 1, se, sp = 1, pstar)sep.freecalc(N, n, c = 1, se, sp = 1, pstar)
N |
population size (scalar) |
n |
sample size (scalar) |
c |
The cut-point number of positives to classify a cluster as positive, default=1, if positives < c result is negative, >= c is positive (scalar) |
se |
test unit sensitivity (scalar) |
sp |
test unit specificity, default=1 (scalar) |
pstar |
design prevalence as a proportion - assumed or target prevalence for detection of disease in the population (scalar) |
population-level sensitivity
# examples of sep.freecalc sep.freecalc(150, 30, 2, 0.9, 0.98, 0.1) sep.freecalc(150, 30, 1, 0.9, 0.98, 0.1)# examples of sep.freecalc sep.freecalc(150, 30, 2, 0.9, 0.98, 0.1) sep.freecalc(150, 30, 1, 0.9, 0.98, 0.1)
Calculates population sensitivity for a finite population and allowing for imperfect test sensitivity and specificity, using Hypergeometric distribution
sep.hp(N, n, c = 1, se, sp = 1, pstar)sep.hp(N, n, c = 1, se, sp = 1, pstar)
N |
population size (scalar or vector of same length as n) |
n |
sample size (scalar or vector) |
c |
The cut-point number of positives to classify a cluster as positive, default=1, if positives < c result is negative, >= c is positive (scalar) |
se |
test unit sensitivity (scalar) |
sp |
test unit specificity, default=1 (scalar) |
pstar |
design prevalence as a proportion (scalar) |
a vector of population-level sensitivities
# examples of sep.hp sep.hp(150, 1:5*10, 2, 0.9, 0.98, 0.1) sep.hp(150, 30, 2, 0.9, 0.98, 15) sep.hp(150, 30, 1, 0.9, 0.98, 15) sep.hp(150, 30, 1, 0.9, 0.98, 0.1)# examples of sep.hp sep.hp(150, 1:5*10, 2, 0.9, 0.98, 0.1) sep.hp(150, 30, 2, 0.9, 0.98, 15) sep.hp(150, 30, 1, 0.9, 0.98, 15) sep.hp(150, 30, 1, 0.9, 0.98, 0.1)
Calculates population sensitivity for detecting disease, assuming imperfect test sensitivity, perfect test specificity and representative sampling, using hypergeometric approximation (assumes known population size)
sep.hypergeo(N, n, d, se = 1, dig = 5)sep.hypergeo(N, n, d, se = 1, dig = 5)
N |
population size, scalar or vector of same length as n |
n |
sample size (number tested), scalar or vector |
d |
expected number of infected units in population (=design prevalence*N rounded to next integer) |
se |
unit sensitivity of test (proportion), scalar or vector of same length as n |
dig |
number of digits for rounding of results |
a vector of population-level sensitivities. if all n <= corresponding N then vector is numeric, otherwise vector is character and elements where n>N are recorded as such
# examples for sep.hypergeo - checked sep.hypergeo(N=100, n=50, d=1, se = 0.92) inf<- 1:5 sens<- 0.8 sep.hypergeo(N=100, n=50, d=inf, se=sens) N<- c(10, 50, 100, 250, 500) sep.hypergeo(se=0.8, N=N, n=c(5, 25, 50, 125, 250), d = ceiling(0.01*N))# examples for sep.hypergeo - checked sep.hypergeo(N=100, n=50, d=1, se = 0.92) inf<- 1:5 sens<- 0.8 sep.hypergeo(N=100, n=50, d=inf, se=sens) N<- c(10, 50, 100, 250, 500) sep.hypergeo(se=0.8, N=N, n=c(5, 25, 50, 125, 250), d = ceiling(0.01*N))
Estimates the population sensitivity of a passive surveillance system. Assumes comprehensive population coverage and samling of representative affected units from infected clusters
sep.passive(step.p, p.inf.u, se, N, n, pstar.c)sep.passive(step.p, p.inf.u, se, N, n, pstar.c)
step.p |
vector or matrix of detection probabilities for each step in the detection process. If a vector each value represents a step probability for a single calculation. If a matrix, columns are step probabilities and rows are simulation iterations. |
p.inf.u |
the probability of infection in units sampled, equivalent to the positive predictive value of clinical signs of disease (for a given prior probability of infection). Either a scalar or vector with length equal to number of rows in step.p. |
se |
unit sensitivity of test (proportion). Either a scalar or vector with length equal to number of rows in step.p. |
N |
population size. Either a scalar or vector with length equal to number of rows in step.p |
n |
number of units tested per cluster reporting suspected disease. Either a scalar or vector with length equal to number of rows in step.p |
pstar.c |
cluster-level design prevalence (proportion). Either a scalar or vector with length equal to number of rows in step.p |
a list of 2 elements, the estimated cluster-level and population-level sensitivities. If step.p is a vector, values are scalars, if step.p is a matrix, values are vectors with length equal to the number of rows in step.p
# examples for sep.passive sep.passive(c(0.1, 0.2, 0.9, 0.99), 0.98, 0.9, 1000, 5, 0.01) sep.passive(c(0.1, 0.5, 0.95, 0.99), 0.98, 0.9, 1000, 5, 0.01) step.p<- matrix(runif(30), nrow=10) p.inf.u<- runif(10, 0.98, 0.999) se<- mc2d::rpert(10, 0.9, 0.95, 0.98) sep.passive(step.p, p.inf.u, se, 10000, 10, 0.02)# examples for sep.passive sep.passive(c(0.1, 0.2, 0.9, 0.99), 0.98, 0.9, 1000, 5, 0.01) sep.passive(c(0.1, 0.5, 0.95, 0.99), 0.98, 0.9, 1000, 5, 0.01) step.p<- matrix(runif(30), nrow=10) p.inf.u<- runif(10, 0.98, 0.999) se<- mc2d::rpert(10, 0.9, 0.95, 0.98) sep.passive(step.p, p.inf.u, se, 10000, 10, 0.02)
Calculates the population sensitivity required to achieve a given value for probability of disease freedom
sep.pfree(prior, pfree)sep.pfree(prior, pfree)
prior |
prior probability of freedom before surveillance (scalar or vector) |
pfree |
desired probability of freedom (scalar or vector) |
a vector of population-level sensitivities
# examples of sep.pfree sep.pfree(0.5, 0.95) sep.pfree(c(0.5, 0.6, 0.7, 0.8, 0.9, 0.95), 0.99) sep.pfree(0.5, c(0.8, 0.9, 0.95, 0.99))# examples of sep.pfree sep.pfree(0.5, 0.95) sep.pfree(c(0.5, 0.6, 0.7, 0.8, 0.9, 0.95), 0.99) sep.pfree(0.5, c(0.8, 0.9, 0.95, 0.99))
Calculates population sensitivity (sep) and population specificity (spp) assuming pooled sampling and allowing for imperfect sensitivity and specificity of the pooled test
sep.pooled(r, k, pstar, pse, psp = 1)sep.pooled(r, k, pstar, pse, psp = 1)
r |
number of pools sampled (scalar or vector) |
k |
pool size (scalar or vector of same length as r) |
pstar |
design prevalence (scalar or vector of same length as r) |
pse |
pool-level sensitivity (scalar or vector of same length as r) |
psp |
pool-level specificity (scalar or vector of same length as r) |
list of 2 elements, vector of sep values and vector of spp values
# examples for sep.pooled sep.pooled(60, 5, 0.01, 1, 1) sep.pooled(4, 10, 0.1, 0.9, 1) sep.pooled(1:10*5, 5, 0.02, 0.9, 0.99) sep.pooled(10, 5, 0.05, c(0.8, 0.9, 0.95, 0.99), 1)# examples for sep.pooled sep.pooled(60, 5, 0.01, 1, 1) sep.pooled(4, 10, 0.1, 0.9, 1) sep.pooled(1:10*5, 5, 0.02, 0.9, 0.99) sep.pooled(10, 5, 0.05, c(0.8, 0.9, 0.95, 0.99), 1)
Calculates the population sensitivity required to achieve a given value for the prior (discounted) probability of disease freedom
sep.prior(prior, p.intro)sep.prior(prior, p.intro)
prior |
prior probability of freedom before surveillance (scalar or vector) |
p.intro |
probability of introduction for time period (scalar or vector equal length to sep) |
a vector of population-level sensitivities
# examples of sep.prior sep.prior(0.95, 0.01) sep.prior(c(0.9, 0.95, 0.98, 0.99), 0.01) sep.prior(0.95, c(0.001, 0.005, 0.01, 0.02, 0.05))# examples of sep.prior sep.prior(0.95, 0.01) sep.prior(c(0.9, 0.95, 0.98, 0.99), 0.01) sep.prior(0.95, c(0.001, 0.005, 0.01, 0.02, 0.05))
Calculates risk-based population sensitivity with a single risk factor, using binomial method (assumes a large population), allows for unit sensitivity to vary among risk strata
sep.rb.bin(pstar, rr, ppr, n, se)sep.rb.bin(pstar, rr, ppr, n, se)
pstar |
design prevalence (scalar) |
rr |
relative risk values (vector of values corresponding to the number of risk strata) |
ppr |
population proportions corresponding to rr values (vector of equal length to rr) |
n |
sample size per risk category (vector same length as rr and ppr) |
se |
unit sensitivity, can vary among risk strata (fixed value or vector same length as rr, ppr, n) |
list of 3 elements, a scalar of population-level sensitivity a vector of EPI values and a vector of corresponding adjusted risks
# examples for sep.rb.bin sep.rb.bin(0.1, c(5, 3, 1), c(0.1, 0.1, 0.8), c(5, 5, 5), 0.9) sep.rb.bin(0.1, c(5, 1), c(0.1, 0.9), c(10, 5), c(0.95, 0.9)) sep.rb.bin(0.1, c(5, 1), c(0.1, 0.9), c(10, 5), c(0.9, 0.9)) sep.rb.bin(0.01, c(5, 1), c(0.1, 0.9), c(90, 50), c(0.9, 0.9))# examples for sep.rb.bin sep.rb.bin(0.1, c(5, 3, 1), c(0.1, 0.1, 0.8), c(5, 5, 5), 0.9) sep.rb.bin(0.1, c(5, 1), c(0.1, 0.9), c(10, 5), c(0.95, 0.9)) sep.rb.bin(0.1, c(5, 1), c(0.1, 0.9), c(10, 5), c(0.9, 0.9)) sep.rb.bin(0.01, c(5, 1), c(0.1, 0.9), c(90, 50), c(0.9, 0.9))
Calculates population sensitivity for a single risk factor and varying unit sensitivity using binomial method (assumes large population)
sep.rb.bin.varse(pstar, rr, ppr, df)sep.rb.bin.varse(pstar, rr, ppr, df)
pstar |
design prevalence (scalar) |
rr |
relative risk values (vector of values corresponding to the number of risk strata) |
ppr |
population proportions corresponding to rr values (vector of equal length to rr) |
df |
dataframe of values for each combination of risk stratum and sensitivity level, col 1 = risk group index, col 2 = unit Se, col 3 = n (sample size for that risk group and unit sensitivity) |
list of 3 elements, a scalar of population-level sensitivity a vector of EPI values and a vector of corresponding adjusted risks
# examples for sep.rb.bin.varse rg<- c(1, 1, 2, 2) se<- c(0.92, 0.85, 0.92, 0.85) n<- c(80, 30, 20, 30) df<- data.frame(rg, se, n) sep.rb.bin.varse(0.01, c(5, 1), c(0.1, 0.9), df) rg<- c(1, 1, 2, 2) se<- c(0.95, 0.8, 0.95, 0.8) n<- c(20, 10, 10, 5) df<- data.frame(rg, se, n) sep.rb.bin.varse(0.05, c(3, 1), c(0.2, 0.8), df) rg<- c(rep(1, 30), rep(2, 15)) se<- c(rep(0.95, 20), rep(0.8, 10), rep(0.95, 10), rep(0.8, 5)) n<- rep(1, 45) df<- data.frame(rg, se, n) sep.rb.bin.varse(0.02, c(3, 1), c(0.2, 0.8), df) rg<- c(1, 2, 3, 1, 2, 3) se<- c(0.95, 0.95, 0.95, 0.8, 0.8, 0.8) n<- c(20, 10, 10, 30, 5, 5) df<- data.frame(rg, se, n) sep.rb.bin.varse(0.01, c(5, 3, 1), c(0.1, 0.3, 0.6), df)# examples for sep.rb.bin.varse rg<- c(1, 1, 2, 2) se<- c(0.92, 0.85, 0.92, 0.85) n<- c(80, 30, 20, 30) df<- data.frame(rg, se, n) sep.rb.bin.varse(0.01, c(5, 1), c(0.1, 0.9), df) rg<- c(1, 1, 2, 2) se<- c(0.95, 0.8, 0.95, 0.8) n<- c(20, 10, 10, 5) df<- data.frame(rg, se, n) sep.rb.bin.varse(0.05, c(3, 1), c(0.2, 0.8), df) rg<- c(rep(1, 30), rep(2, 15)) se<- c(rep(0.95, 20), rep(0.8, 10), rep(0.95, 10), rep(0.8, 5)) n<- rep(1, 45) df<- data.frame(rg, se, n) sep.rb.bin.varse(0.02, c(3, 1), c(0.2, 0.8), df) rg<- c(1, 2, 3, 1, 2, 3) se<- c(0.95, 0.95, 0.95, 0.8, 0.8, 0.8) n<- c(20, 10, 10, 30, 5, 5) df<- data.frame(rg, se, n) sep.rb.bin.varse(0.01, c(5, 3, 1), c(0.1, 0.3, 0.6), df)
Calculates risk-based population sensitivity with a single risk factor, using the hypergeometric method (assuming a finite and known population size), allows for unit sensitivity to vary among risk strata
sep.rb.hypergeo(pstar, rr, N, n, se)sep.rb.hypergeo(pstar, rr, N, n, se)
pstar |
design prevalence (scalar) |
rr |
relative risk values (vector of values corresponding to the number of risk strata) |
N |
Population size per risk category (vector same length as rr and ppr) |
n |
sample size per risk category (vector same length as rr and ppr) |
se |
unit sensitivity, can vary among risk strata (fixed value or a vector the same length as rr, ppr, n) |
list of 3 elements, a scalar of population-level sensitivity a vector of EPI values and a vector of corresponding adjusted risks
# examples for sep.rb.bin sep.rb.hypergeo(0.1, c(5, 3, 1), c(10, 10, 80), c(5, 5, 5), 0.9) sep.rb.hypergeo(0.1, c(5, 1), c(15, 140), c(10, 5), c(0.95, 0.9)) sep.rb.hypergeo(0.1, c(5, 1), c(23, 180), c(10, 5), c(0.9, 0.9)) sep.rb.hypergeo(0.01, c(5, 1), c(100, 900), c(90, 50), c(0.9, 0.9))# examples for sep.rb.bin sep.rb.hypergeo(0.1, c(5, 3, 1), c(10, 10, 80), c(5, 5, 5), 0.9) sep.rb.hypergeo(0.1, c(5, 1), c(15, 140), c(10, 5), c(0.95, 0.9)) sep.rb.hypergeo(0.1, c(5, 1), c(23, 180), c(10, 5), c(0.9, 0.9)) sep.rb.hypergeo(0.01, c(5, 1), c(100, 900), c(90, 50), c(0.9, 0.9))
Calculates population sensitivity for a single risk factor and varying unit sensitivity using hypergeometric approximation method (assumes known population size)
sep.rb.hypergeo.varse(pstar, rr, N, df)sep.rb.hypergeo.varse(pstar, rr, N, df)
pstar |
design prevalence (scalar) |
rr |
relative risk values (vector of values corresponding to the number of risk strata) |
N |
vector of population size for each risk group, corresponding to rr values (vector of equal length to rr) |
df |
dataframe of values for each combination of risk stratum and sensitivity level, col 1 = risk group index, col 2 = unit Se, col 3 = n (sample size for risk group and unit sensitivity) |
list of 5 elements, a scalar of population-level sensitivity a vector of EPI values, a vector of corresponding Adjusted risks a vector of sample sizes (n) per risk group and a vector of mean unit sensitivities per risk group
# examples for sep.rb.hypergeo.varse rg<- c(1, 1, 2, 2) se<- c(0.92, 0.85, 0.92, 0.85) n<- c(80, 30, 20, 30) df<- data.frame(rg, se, n) sep.rb.hypergeo.varse(0.01, c(5, 1), c(200, 1800), df) rg<- c(1, 1, 2, 2) se<- c(0.95, 0.8, 0.95, 0.8) n<- c(20, 10, 10, 5) df<- data.frame(rg, se, n) sep.rb.hypergeo.varse(0.05, c(3, 1), c(100, 400), df) rg<- c(rep(1, 30), rep(2, 15)) se<- c(rep(0.95, 20), rep(0.8, 10), rep(0.95, 10), rep(0.8, 5)) n<- rep(1, 45) df<- data.frame(rg, se, n) sep.rb.hypergeo.varse(0.02, c(3, 1), c(100, 400), df) rg<- c(1, 2, 3, 1, 2, 3) se<- c(0.95, 0.95, 0.95, 0.8, 0.8, 0.8) n<- c(20, 10, 10, 30, 5, 5) df<- data.frame(rg, se, n) sep.rb.hypergeo.varse(0.01, c(5, 3, 1), c(100, 300, 600), df)# examples for sep.rb.hypergeo.varse rg<- c(1, 1, 2, 2) se<- c(0.92, 0.85, 0.92, 0.85) n<- c(80, 30, 20, 30) df<- data.frame(rg, se, n) sep.rb.hypergeo.varse(0.01, c(5, 1), c(200, 1800), df) rg<- c(1, 1, 2, 2) se<- c(0.95, 0.8, 0.95, 0.8) n<- c(20, 10, 10, 5) df<- data.frame(rg, se, n) sep.rb.hypergeo.varse(0.05, c(3, 1), c(100, 400), df) rg<- c(rep(1, 30), rep(2, 15)) se<- c(rep(0.95, 20), rep(0.8, 10), rep(0.95, 10), rep(0.8, 5)) n<- rep(1, 45) df<- data.frame(rg, se, n) sep.rb.hypergeo.varse(0.02, c(3, 1), c(100, 400), df) rg<- c(1, 2, 3, 1, 2, 3) se<- c(0.95, 0.95, 0.95, 0.8, 0.8, 0.8) n<- c(20, 10, 10, 30, 5, 5) df<- data.frame(rg, se, n) sep.rb.hypergeo.varse(0.01, c(5, 3, 1), c(100, 300, 600), df)
Calculates risk-based population sensitivity for two risk factors, using binomial method (assumes a large population)
sep.rb2.binom(pstar, rr1, ppr1, rr2, ppr2, n, se)sep.rb2.binom(pstar, rr1, ppr1, rr2, ppr2, n, se)
pstar |
design prevalence (scalar) |
rr1 |
relative risks for first level risk factor (vector of values corresponding to the number of risk strata) |
ppr1 |
population proportions for first level risk factor (vector of same length as rr1) |
rr2 |
relative risks for second level risk factor, matrix, rows = levels of rr1, cols = levels of rr2 |
ppr2 |
population proportions for second level risk factor, matrix, rows = levels of rr1, cols = levels of rr2 |
n |
matrix of number tested for each risk group (rows = levels of rr1, cols = levels of rr2) |
se |
test unit sensitivity (scalar) |
list of 4 elements, a scalar of population-level sensitivity a matrix of EPI values, a vector of corresponding Adjusted risks for the first risk factor and a matrix of adjusted risks for the second risk factor
# examples for sep.rb2.binom pstar<- 0.01 rr1<- c(3, 1) ppr1<- c(0.2, 0.8) rr2<- rbind(c(4,1), c(4,1)) ppr2<- rbind(c(0.1, 0.9), c(0.3, 0.7)) se<- 0.8 n<- rbind(c(50, 20), c(20, 10)) sep.rb2.binom(pstar, rr1, ppr1, rr2, ppr2, n, se)# examples for sep.rb2.binom pstar<- 0.01 rr1<- c(3, 1) ppr1<- c(0.2, 0.8) rr2<- rbind(c(4,1), c(4,1)) ppr2<- rbind(c(0.1, 0.9), c(0.3, 0.7)) se<- 0.8 n<- rbind(c(50, 20), c(20, 10)) sep.rb2.binom(pstar, rr1, ppr1, rr2, ppr2, n, se)
Calculates risk-based population sensitivity for two risk factors, using hypergeometric approximation method (assumes a known population size)
sep.rb2.hypergeo(pstar, rr1, rr2, N, n, se)sep.rb2.hypergeo(pstar, rr1, rr2, N, n, se)
pstar |
design prevalence (scalar) |
rr1 |
relative risks for first level risk factor (vector of values corresponding to the number of risk strata) |
rr2 |
relative risks for second level risk factor, matrix, rows = levels of rr1, cols = levels of rr2 |
N |
matrix of population size for each risk group (rows = levels of rr1, cols = levels of rr2) |
n |
matrix of number tested (sample size) for each risk group (rows = levels of rr1, cols = levels of rr2) |
se |
test unit sensitivity (scalar) |
list of 6 elements, a scalar of population-level sensitivity a matrix of EPI values, a vector of corresponding Adjusted risks for the first risk factor and a matrix of adjusted risks for the second risk factor, a vector of population proportions for the first risk factor and a matrix of population proportions for the second risk factor
# examples for sep.rb2.hypergeo pstar<- 0.01 rr1<- c(3, 1) rr2<- rbind(c(4,1), c(4,1)) N<- rbind(c(100, 500), c(300, 1000)) n<- rbind(c(50, 20), c(20, 10)) se<- 0.8 sep.rb2.hypergeo(pstar, rr1, rr2, N, n, se)# examples for sep.rb2.hypergeo pstar<- 0.01 rr1<- c(3, 1) rr2<- rbind(c(4,1), c(4,1)) N<- rbind(c(100, 500), c(300, 1000)) n<- rbind(c(50, 20), c(20, 10)) se<- 0.8 sep.rb2.hypergeo(pstar, rr1, rr2, N, n, se)
Calculates population-level (system) sensitivity for representative 2-stage sampling (sampling of clusters and units within clusters), assuming imperfect test sensitivity and perfect test specificity
sep.sys(H = NA, N = NA, n, pstar.c, pstar.u, se = 1)sep.sys(H = NA, N = NA, n, pstar.c, pstar.u, se = 1)
H |
population size = number of clusters in the population, default = NA |
N |
population size within clusters, scalar or a vector of same length as n, default = NA |
n |
sample size (vector of number tested per cluster) |
pstar.c |
cluster (herd) level design prevalence, scalar, either proportion or integer |
pstar.u |
unit (animal) level design prevalence, scalar, either proportion or integer |
se |
unit sensitivity of test (proportion), scalar, default = 1 |
list of 6 elements, 1) population level sensitivity, 2) vector of cluster-level sensitivities, 3) N, 4) n, 5) vector of design prevalences and 6) unit sensitivity
if pstar.c is not a proportion N must be provided (and N>=n)
# examples for sep.sys - checked H<- 500 N<- rep(1000, 150) N[5]<- NA n<- rep(30, 150) pstar.u<- 0.1 pstar.c<- 0.01 se<- 0.98 sep.sys(H, N, n, pstar.c, pstar.u, se) sep.sys(NA, N, n, 0.02, 0.05, 0.95) N<- round(runif(105)*900+100) n<- round(runif(105)*30+10) sse<- sep.sys(1000, N, n, 0.02, 0.05, 0.9) data.frame(N, n, sse[[2]])# examples for sep.sys - checked H<- 500 N<- rep(1000, 150) N[5]<- NA n<- rep(30, 150) pstar.u<- 0.1 pstar.c<- 0.01 se<- 0.98 sep.sys(H, N, n, pstar.c, pstar.u, se) sep.sys(NA, N, n, 0.02, 0.05, 0.95) N<- round(runif(105)*900+100) n<- round(runif(105)*30+10) sse<- sep.sys(1000, N, n, 0.02, 0.05, 0.9) data.frame(N, n, sse[[2]])
Calculates population-level sensitivity where unit sensitivity varies and using the appropriate method, depending on whether or not N provided (hypergeometric if N provided, binomial otherwise), assuming perfect test specificity and representative sampling
sep.var.se(N = NA, se, pstar)sep.var.se(N = NA, se, pstar)
N |
population size (number of units or clusters), N must be >= length(se)) or NA if unknown |
se |
vector of unit sensitivity values (proportion) for each unit sampled |
pstar |
specified design prevalence (scalar) |
a scalar of population-level sensitivity
# examples of sep.var.se - checked sens<- c(rep(0.9, 50), rep(0.95, 100)) sep.var.se(NA, sens, 0.01) sep.var.se(se=sens, pstar=0.01) sep.var.se(N=500, sens, 0.01) sep.var.se(NA, runif(150, 0.95, 0.99), 0.02) sep.var.se(500, runif(150, 0.95, 0.99), 0.02)# examples of sep.var.se - checked sens<- c(rep(0.9, 50), rep(0.95, 100)) sep.var.se(NA, sens, 0.01) sep.var.se(se=sens, pstar=0.01) sep.var.se(N=500, sens, 0.01) sep.var.se(NA, runif(150, 0.95, 0.99), 0.02) sep.var.se(500, runif(150, 0.95, 0.99), 0.02)
Calculates the combined specificity for multiple tests interpreted in parallel (assuming independence)
sp.parallel(sp)sp.parallel(sp)
sp |
vector of unit specificity values |
scalar of combined specificity, assuming independence
# examples for sp.parallel sp.parallel(c(0.99, 0.95, 0.8))# examples for sp.parallel sp.parallel(c(0.99, 0.95, 0.8))
Calculates the combined specificity for multiple tests interpreted in series (assuming independence)
sp.series(sp)sp.series(sp)
sp |
vector of unit specificity values |
scalar of combined specificity, assuming independence
# examples for sp.series sp.series(c(0.99, 0.95, 0.8))# examples for sp.series sp.series(c(0.99, 0.95, 0.8))
Calculates population specificity for a large or unknown population, using the Binomial distribution and adjusting for cut-point number of positives
sph.binom(n, c = 1, sp)sph.binom(n, c = 1, sp)
n |
sample size (scalar or vector) |
c |
The cut-point number of positives to classify a cluster as positive, default=1, if positives < c result is negative, >= c is positive (scalar or vector of same length as n) |
sp |
test unit specificity (scalar or vector of same length as n) |
a vector of population-level specificities
# examples for sph.imperfect.sp sph.binom(30, 2, 0.98) sph.binom(30, 1, 0.98) sph.binom(1:5*10, 2, 0.98) sph.binom(100, 1:5, 0.98) sph.binom(100, 3, 95:100/100) sph.binom(c(5, 10, 15, 20, 30, 50, 100, 200), 2, 0.98)# examples for sph.imperfect.sp sph.binom(30, 2, 0.98) sph.binom(30, 1, 0.98) sph.binom(1:5*10, 2, 0.98) sph.binom(100, 1:5, 0.98) sph.binom(100, 3, 95:100/100) sph.binom(c(5, 10, 15, 20, 30, 50, 100, 200), 2, 0.98)
Calculates population specificity for a finite population and imperfect test, using Hypergeometric distribution
sph.hp(N, n, c = 1, sp)sph.hp(N, n, c = 1, sp)
N |
population size (scalar or vector of same length as n) |
n |
sample size (scalar or vector) |
c |
The cut-point number of positives to classify a cluster as positive, default=1, if positives < c result is negative, >= c is positive (scalar or vector of same length as n) |
sp |
test unit specificity (scalar or vector of same length as n) |
a vector of population-level specificities
# examples of sph.hp sph.hp(150, 30, 2, 0.98) sph.hp(150, 30, 1, 0.98) sph.hp(150, 1:5*10, 2, 0.98) sph.hp(500, 30, 2, 95:100/100)# examples of sph.hp sph.hp(150, 30, 2, 0.98) sph.hp(150, 30, 1, 0.98) sph.hp(150, 1:5*10, 2, 0.98) sph.hp(500, 30, 2, 95:100/100)
Calculates population specificity assuming representative sampling
spp(n, sp)spp(n, sp)
n |
sample size (number tested), integer, scalar or vector |
sp |
unit specificity of test (proportion), scalar or vector of same length as n |
a vector of population-level specificities
# examples for spp - checked spp(10, 0.9) spp(c(10, 20, 50, 100), 0.99) spp(100, c(0.999, 0.99, 0.98, 0.95, 0.9))# examples for spp - checked spp(10, 0.9) spp(c(10, 20, 50, 100), 0.99) spp(100, c(0.999, 0.99, 0.98, 0.95, 0.9))
Calculates overall system sensitivity for multiple components, accounting for lack of independence (overlap) between components
sse.combined(C = NA, pstar.c, rr, ppr, sep)sse.combined(C = NA, pstar.c, rr, ppr, sep)
C |
population sizes (number of clusters) for each risk group, NA or vector of same length as rr |
pstar.c |
cluster level design prevalence (scalar) |
rr |
cluster level relative risks (vector, length equal to the number of risk strata) |
ppr |
cluster level population proportions (optional), not required if C is specified (NA or vector of same length as rr) |
sep |
sep values for clusters in each component and corresponding risk group. A list with multiple elements, each element is a dataframe of sep values from a separate component, first column= clusterid, 2nd =cluster-level risk group index, 3rd col = sep |
list of 2 elements, a matrix (or vector if C not specified) of population-level (surveillance system) sensitivities (binomial and hypergeometric and adjusted vs unadjusted) and a matrix of adjusted and unadjusted component sensitivities for each component
# example for sse.combined (checked in excel combined components.xlsx) C<- c(300, 1200) pstar<- 0.01 rr<- c(3,1) ppr<- c(0.2, 0.8) comp1<- data.frame(id=1:100, rg=c(rep(1,50), rep(2,50)), cse=rep(0.5,100)) comp2<- data.frame(id=seq(2, 120, by=2), rg=c(rep(1,25), rep(2,35)), cse=runif(60, 0.5, 0.8)) comp3<- data.frame(id=seq(5, 120, by=5), rg=c(rep(1,10), rep(2,14)), cse=runif(24, 0.7, 1)) sep<- list(comp1, comp2, comp3) sse.combined(C, pstar, rr, sep = sep) sse.combined(C=NA, pstar, rr, ppr, sep = sep)# example for sse.combined (checked in excel combined components.xlsx) C<- c(300, 1200) pstar<- 0.01 rr<- c(3,1) ppr<- c(0.2, 0.8) comp1<- data.frame(id=1:100, rg=c(rep(1,50), rep(2,50)), cse=rep(0.5,100)) comp2<- data.frame(id=seq(2, 120, by=2), rg=c(rep(1,25), rep(2,35)), cse=runif(60, 0.5, 0.8)) comp3<- data.frame(id=seq(5, 120, by=5), rg=c(rep(1,10), rep(2,14)), cse=runif(24, 0.7, 1)) sep<- list(comp1, comp2, comp3) sse.combined(C, pstar, rr, sep = sep) sse.combined(C=NA, pstar, rr, ppr, sep = sep)
Calculates system sensitivity for 2 stage risk-based sampling, llowing for a single risk factor at each stage and using either binomial or hypergeometric approxiation
sse.rb.2stage(C = NA, pstar.c, pstar.u, rr.c, ppr.c, rr.u, ppr.u, N = NA, n, rg, se)sse.rb.2stage(C = NA, pstar.c, pstar.u, rr.c, ppr.c, rr.u, ppr.u, N = NA, n, rg, se)
C |
Population size (number of clusters), NA = unknown (default) |
pstar.c |
cluster level design prevalence (scalar) |
pstar.u |
unit level design prevalence (scalar) |
rr.c |
cluster level relative risks (vector with length corresponding to the number of risk strata), use rr.c = c(1,1) if risk factor does not apply |
ppr.c |
cluster level population proportions for risk categories (vector), NA if no cluster level risk factor |
rr.u |
unit level relative risks (vector with length corresponding to the number of risk strata), use rr.u = c(1,1) if risk factor does not apply |
ppr.u |
unit level population proportions for each risk group (optional) matrix, 1 row for each cluster, columns = unit level risk groups, not required if N is provided |
N |
population size per risk group for each cluster, NA or matrix of N for each risk group for each cluster, N=NA means cluster sizes not provided |
n |
sample size per risk group for each cluster sampled, matrix, 1 row for each cluster, columns = unit level risk groups |
rg |
vector of cluster level risk group (index) for each cluster |
se |
unit sensitivity for each cluster, scalar or vector of values for each cluster, equal in length to n |
list of 2 elements, a scalar of population-level (surveillance system) sensitivity and a vector of cluster-level sensitivities
# examples for sse.rb.2stage pstar.c<- 0.02 pstar.u<- 0.1 rr.c<- c(5, 1) ppr.c<- c(0.1, 0.9) rr.u<- c(3, 1) se<- 0.9 n<- cbind(rep(10, 50), rep(5, 50)) rg<- c(rep(1, 30), rep(2, 20)) ppr.u<- cbind(rep(0.2, 50), rep(0.8, 50)) N<- cbind(rep(30, 50), rep(120, 50)) C<- 500 sse.rb.2stage(C=NA, pstar.c, pstar.u, rr.c, ppr.c, rr.u, ppr.u, N=NA, n, rg, se) sse.rb.2stage(C, pstar.c, pstar.u, rr.c, ppr.c, rr.u, ppr.u, N=NA, n, rg, se) sse.rb.2stage(C=NA, pstar.c, pstar.u, rr.c, ppr.c, rr.u, ppr.u, N, n, rg, se) sse.rb.2stage(C, pstar.c, pstar.u, rr.c, ppr.c, rr.u, ppr.u, N, n, rg, se)# examples for sse.rb.2stage pstar.c<- 0.02 pstar.u<- 0.1 rr.c<- c(5, 1) ppr.c<- c(0.1, 0.9) rr.u<- c(3, 1) se<- 0.9 n<- cbind(rep(10, 50), rep(5, 50)) rg<- c(rep(1, 30), rep(2, 20)) ppr.u<- cbind(rep(0.2, 50), rep(0.8, 50)) N<- cbind(rep(30, 50), rep(120, 50)) C<- 500 sse.rb.2stage(C=NA, pstar.c, pstar.u, rr.c, ppr.c, rr.u, ppr.u, N=NA, n, rg, se) sse.rb.2stage(C, pstar.c, pstar.u, rr.c, ppr.c, rr.u, ppr.u, N=NA, n, rg, se) sse.rb.2stage(C=NA, pstar.c, pstar.u, rr.c, ppr.c, rr.u, ppr.u, N, n, rg, se) sse.rb.2stage(C, pstar.c, pstar.u, rr.c, ppr.c, rr.u, ppr.u, N, n, rg, se)
Estimates true prevalence and confidence limits for given sample size and result, according to specified method
tp(x, n, se, sp, type = "blaker", conf = 0.95)tp(x, n, se, sp, type = "blaker", conf = 0.95)
x |
number of positive units (scalar) |
n |
sample size (no. units sampled) (scalar) |
se |
test sensitivity (scalar) |
sp |
test specificity (scalar) |
type |
method for estimating CI, one of c("normal", "c-p", "sterne", "blaker", "wilson", "all") |
conf |
desired level of confidence for CI, default = 0.95 (scalar) |
list with 2 elements, a matrix of apparent prevalence and lower and upper confidence limits and a matrix of true prevalence and lower and upper confidence limits using the chosen method(s)
# examples for tp x<- 20 n<- 120 se<- 0.9 sp<- 0.99 conf<- 0.95 tp(x, n, se, sp, "all") tp(x, n, se, sp, "c-p") tp(x, n, 0.95, 0.9, "c-p")# examples for tp x<- 20 n<- 120 se<- 0.9 sp<- 0.99 conf<- 0.95 tp(x, n, se, sp, "all") tp(x, n, se, sp, "c-p") tp(x, n, 0.95, 0.9, "c-p")
Estimates true prevalence and confidence limits for estimates based on normal approximation
tp.normal(x, n, se, sp, conf = 0.95)tp.normal(x, n, se, sp, conf = 0.95)
x |
number of positive results in sample (scalar or vector) |
n |
sample size (scalar or vector) |
se |
test unit sensitivity (scalar or vector) |
sp |
test unit specificity (scalar or vector) |
conf |
desired level of confidence for CI, default = 0.95 (scalar or vector) |
list with 2 elements, a matrix of apparent prevalence and wilson lower and upper confidence limits and a matrix of true prevalence and normal approximation lower and upper confidence limits
# examples for tp.normal tp.normal(25, 120, 0.9, 0.99) tp.normal(seq(5, 25, by=5), 120, 0.9, 0.99)# examples for tp.normal tp.normal(25, 120, 0.9, 0.99) tp.normal(seq(5, 25, by=5), 120, 0.9, 0.99)