Adjusted risk

Description

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

Usage

adj.risk(rr, ppr)

Arguments

Value

vector of adjusted risk values (in order corresponding to rr)

Examples

# 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))

Adjusted risk for simulation models

Description

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.

Usage

adj.risk.sim(rr, ppr)

Arguments

Value

matrix of adjusted risk values (in order corresponding to rr)

Examples

# 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))

Apparent prevalence

Description

Estimates apparent prevalence and confidence limits for given sample size and result, assuming representative sampling

Usage

ap(x, n, type = "wilson", conf = 0.95)

Arguments

Value

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

# 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")

Agresti-Coull confidence limits

Description

Calculates Agresti-Coull confidence limits for a simple proportion (apparent prevalence)

Usage

binom.agresti(x, n, conf = 0.95)

Arguments

Value

a dataframe with 6 columns, x, n, proportion, lower confidence limit, upper confidence limit, confidence level and CI method

Examples

# test binom.agresti
binom.agresti(25, 200)
binom.agresti(seq(10, 100, 10), 200)
binom.agresti(50, seq(100, 1000, 100))

Clopper-Pearson exact confidence limits

Description

Calculates Clopper-Pearson exact binomial confidence limits for a simple proportion (apparent prevalence)

Usage

binom.cp(x, n, conf = 0.95)

Arguments

Value

a dataframe with 6 columns, x, n, proportion, lower confidence limit, upper confidence limit, confidence level and CI method

Examples

# test binom.cp
binom.cp(25, 200)
binom.cp(seq(10, 100, 10), 200)
binom.cp(50, seq(100, 1000, 100))

Jeffreys confidence limits

Description

Calculates Jeffreys confidence limits for a simple proportion (apparent prevalence)

Usage

binom.jeffreys(x, n, conf = 0.95)

Arguments

Value

a dataframe with 6 columns, x, n, proportion, lower confidence limit, upper confidence limit, confidence level and CI method

Examples

# test binom.jeffreys
binom.jeffreys(25, 200)
binom.jeffreys(seq(10, 100, 10), 200)
binom.jeffreys(50, seq(100, 1000, 100))

Discounted prior probability of freedom

Description

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

Usage

disc.prior(prior, p.intro)

Arguments

Value

vector of discounted prior probabilities of freedom

Examples

# 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)

Effective probability of infection (EPI)

Description

Calculates effective probability of infection (adjusted design prevalence) for each risk group for risk-based surveillance activities

Usage

epi.calc(pstar, rr, ppr)

Arguments

Value

list of 2 elements, a vector of EPI values and a vector of corresponding adjusted risks (in corresponding order to rr)

Examples

# 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))

2-stage freedom sample size

Description

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

Usage

n.2stage(H = NA, N = NA, sep.sys = 0.95, sep.c, pstar.c, pstar.u,
  se = 1)

Arguments

Value

a list of 2 elements, the number of clusters to sample and a vector of sample sizes per cluster

Examples

# 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)

Sample size for apparent prevalence

Description

Calculates sample size for estimating apparent prevalence (simple proportion)

Usage

n.ap(p, precision, conf = 0.95)

Arguments

Value

a vector of sample sizes

Examples

# 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))

Binomial sample size

Description

Calculates sample size for demonstrating freedom or detecting disease using binomial approach and assuming imperfect test sensitivity, perfect test specificity and representative sampling

Usage

n.binom(sep, pstar, se = 1)

Arguments

Value

vector of sample sizes

Examples

# 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)

Freecalc optimum sample size and cut-point number of positives

Description

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

Usage

n.c.freecalc(N, sep = 0.95, c = 1, se, sp = 1, pstar,
  minSpH = 0.95)

Arguments

Value

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

# 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)

Hypergeometric (HerdPlus) optimum sample size and cut-point number of positives

Description

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

Usage

n.c.hp(N, sep = 0.95, c = 1, se, sp = 1, pstar, minSpH = 0.95)

Arguments

Value

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

# 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)

Freecalc sample size for a finite population and specified cut-point number of positives

Description

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

Usage

n.freecalc(N, sep = 0.95, c = 1, se, sp = 1, pstar, minSpH = 0.95)

Arguments

Value

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

# 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)

Freedom sample size

Description

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

Usage

n.freedom(N = NA, sep = 0.95, pstar, se = 1)

Arguments

Value

vector of sample sizes, NA if N is specified and n>N

Examples

# 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))

Hypergeometric (HerdPlus) sample size for finite population and specified cut-point number of positives

Description

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

Usage

n.hp(N, sep = 0.95, c = 1, se, sp = 1, pstar, minSpH = 0.95)

Arguments

Value

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

# 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)

Hypergeometric sample size

Description

Calculates sample size for demonstrating freedom or detecting disease using hypergeometric approximation and assuming imperfect test sensitivity, perfect test specificity and representative sampling

Usage

n.hypergeo(sep, N, d, se = 1)

Arguments

Value

vector of sample sizes, NA if n>N

Examples

# 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)

Sample size to achieve desired (posterior) probability of freedom

Description

Calculates the sample size required to achieve a given value for probability of disease freedom

Usage

n.pfree(pfree, prior, p.intro, pstar, se, N = NA)

Arguments

Value

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

# 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)

Sample size for pooled testing for freedom

Description

Calculates sample size to achieve desired population-level sensitivity, assuming pooled sampling and allowing for imperfect sensitivity and specificity of the pooled test

Usage

n.pooled(sep, k, pstar, pse, psp = 1)

Arguments

Value

vector of sample sizes

Examples

# 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)

Risk-based sample size

Description

Calculates sample size for risk-based sampling for a single risk factor and using binomial method

Usage

n.rb(pstar, rr, ppr, spr, se, sep)

Arguments

Value

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

# 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)

sample size for 2-stage risk-based surveillance, risk factor at cluster level only

Description

Calculates sample size required (clusters and units) for a 2-stage risk-based survey with a single risk factor at the cluster level only.

Usage

n.rb.2stage.1(rr, ppr, spr, pstar.c, pstar.u, se = 1, sep.c = 0.95,
  sep.sys = 0.95)

Arguments

Value

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

Examples

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)

Sample size for 2-stage risk-based surveillance, allowing for risk factors at either or both cluster and unit level

Description

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.

Usage

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)

Arguments

Value

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.

Examples

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)

Risk-based sample size for varying unit sensitivity

Description

Calculates sample size for risk-based sampling for a single risk factor and varying unit sensitivity, using binomial method

Usage

n.rb.varse(pstar, rr, ppr, spr, se, spr.rg, sep)

Arguments

Value

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

# 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)

Sample size for true prevalence

Description

Calculates sample size for estimating true prevalence using normal approximation

Usage

n.tp(p, se, sp, precision, conf = 0.95)

Arguments

Value

a vector of sample sizes

Examples

# 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))

Probability of freedom for single time period

Description

Calculates the posterior probability (confidence) of disease freedom (negative predictive value) for a single time period

Usage

pfree.1(sep, p.intro, prior = 0.5)

Arguments

Value

data.frame with columns for sep, p.intro, discounted prior, pfree, pfree.equ and prior.equ

Examples

# 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)

Probability of freedom over time

Description

Calculates the probability (confidence) of disease freedom for given prior, sep and p.intro over 1 or more time periods

Usage

pfree.calc(sep, p.intro, prior = 0.5, discount.1 = TRUE)

Arguments

Value

data.frame with columns for sep, p.intro, discounted prior, probability of freedom, equilibrium probability of freedom and equilibrium prior

Examples

# 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)

Equilibrium probability of freedom

Description

Calculates equilibrium probability of disease freedom and equilibrium prior probability of freedom, after discounting for probability of introduction

Usage

pfree.equ(sep, p.intro)

Arguments

Value

a list of 2 vectors, equilibrium posterior probability of freedom and equilibrium prior (discounted) probability of freedom

Examples

# 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))

Design prevalence back-calculation

Description

Calculates design prevalence required for given sample size and desired population-level sensitivity, assuming imperfect test sensitivity, perfect test specificity and representative sampling

Usage

pstar.calc(N = NA, n, sep, se)

Arguments

Value

vector of design prevalence values

Examples

# 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))

Sensitivity of tests in parallel

Description

Calculates the combined sensitivity for multiple tests interpreted in parallel (assuming independence)

Usage

se.parallel(se)

Arguments

Value

scalar of combined sensitivity, assuming independence

Examples

# examples for se.parallel
se.parallel(c(0.99, 0.95, 0.8))

Sensitivity of tests in series

Description

Calculates the combined sensitivity for multiple tests interpreted in series (assuming independence)

Usage

se.series(se)

Arguments

Value

scalar of combined sensitivity, assuming independence

Examples

# examples for se.series
se.series(c(0.99, 0.95, 0.8))

Population sensitivity

Description

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

Usage

sep(N = NA, n, pstar, se = 1, dig = 5)

Arguments

Value

a vector of population-level sensitivities

Examples

# 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)

Binomial Population sensitivity

Description

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)

Usage

sep.binom(n, pstar, se = 1, sp = 1, dig = 5)

Arguments

Value

vector of population-level sensitivities

Examples

# 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)

Binomial population sensitivity for imperfect test

Description

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

Usage

sep.binom.imperfect(n, c = 1, se, sp = 1, pstar)

Arguments

Value

a vector of population-level sensitivities

Examples

# 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)

Population sensitivity for census (all units tested)

Description

Calculates population sensitivity for detecting disease assuming imperfect test sensitivity, perfect test specificity and a census of all units in the population

Usage

sep.exact(d = 1, se = 1, dig = 5)

Arguments

Value

vector of population-level sensitivities

Examples

# 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)))

FreeCalc population sensitivity for imperfect test

Description

Calculates population sensitivity for a finite population and allowing for imperfect test sensitivity and specificity, using Freecalc method

Usage

sep.freecalc(N, n, c = 1, se, sp = 1, pstar)

Arguments

Value

population-level sensitivity

Examples

# 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)

Hypergeometric (HerdPlus) population sensitivity for imperfect test

Description

Calculates population sensitivity for a finite population and allowing for imperfect test sensitivity and specificity, using Hypergeometric distribution

Usage

sep.hp(N, n, c = 1, se, sp = 1, pstar)

Arguments

Value

a vector of population-level sensitivities

Examples

# 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)

Hypergeometric Population sensitivity

Description

Calculates population sensitivity for detecting disease, assuming imperfect test sensitivity, perfect test specificity and representative sampling, using hypergeometric approximation (assumes known population size)

Usage

sep.hypergeo(N, n, d, se = 1, dig = 5)

Arguments

Value

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

# 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))

Passive surveillance sensitivity

Description

Estimates the population sensitivity of a passive surveillance system. Assumes comprehensive population coverage and samling of representative affected units from infected clusters

Usage

sep.passive(step.p, p.inf.u, se, N, n, pstar.c)

Arguments

Value

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

# 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)

Population sensitivity to achieve desired (posterior) probability of freedom

Description

Calculates the population sensitivity required to achieve a given value for probability of disease freedom

Usage

sep.pfree(prior, pfree)

Arguments

Value

a vector of population-level sensitivities

Examples

# 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))

Pooled population sensitivity

Description

Calculates population sensitivity (sep) and population specificity (spp) assuming pooled sampling and allowing for imperfect sensitivity and specificity of the pooled test

Usage

sep.pooled(r, k, pstar, pse, psp = 1)

Arguments

Value

list of 2 elements, vector of sep values and vector of spp values

Examples

# 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)

Population sensitivity to achieve desired prior probability of freedom

Description

Calculates the population sensitivity required to achieve a given value for the prior (discounted) probability of disease freedom

Usage

sep.prior(prior, p.intro)

Arguments

Value

a vector of population-level sensitivities

Examples

# 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))

Binomial risk-based population sensitivity

Description

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

Usage

sep.rb.bin(pstar, rr, ppr, n, se)

Arguments

Value

list of 3 elements, a scalar of population-level sensitivity a vector of EPI values and a vector of corresponding adjusted risks

Examples

# 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))

Binomial risk-based population sensitivity for varying unit sensitivity

Description

Calculates population sensitivity for a single risk factor and varying unit sensitivity using binomial method (assumes large population)

Usage

sep.rb.bin.varse(pstar, rr, ppr, df)

Arguments

Value

list of 3 elements, a scalar of population-level sensitivity a vector of EPI values and a vector of corresponding adjusted risks

Examples

# 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)

Hypergeometric risk-based population sensitivity

Description

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

Usage

sep.rb.hypergeo(pstar, rr, N, n, se)

Arguments

Value

list of 3 elements, a scalar of population-level sensitivity a vector of EPI values and a vector of corresponding adjusted risks

Examples

# 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))

Hypergeometric risk-based population sensitivity for varying unit sensitivity

Description

Calculates population sensitivity for a single risk factor and varying unit sensitivity using hypergeometric approximation method (assumes known population size)

Usage

sep.rb.hypergeo.varse(pstar, rr, N, df)

Arguments

Value

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

# 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)

Binomial risk-based population sensitivity for 2 risk factors

Description

Calculates risk-based population sensitivity for two risk factors, using binomial method (assumes a large population)

Usage

sep.rb2.binom(pstar, rr1, ppr1, rr2, ppr2, n, se)

Arguments

Value

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

# 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)

Hypergeometric risk-based population sensitivity for 2 risk factors

Description

Calculates risk-based population sensitivity for two risk factors, using hypergeometric approximation method (assumes a known population size)

Usage

sep.rb2.hypergeo(pstar, rr1, rr2, N, n, se)

Arguments

Value

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

# 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)

2-stage population sensitivity

Description

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

Usage

sep.sys(H = NA, N = NA, n, pstar.c, pstar.u, se = 1)

Arguments

Value

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

Note

if pstar.c is not a proportion N must be provided (and N>=n)

Examples

# 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]])

Population sensitivity for varying unit sensitivity

Description

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

Usage

sep.var.se(N = NA, se, pstar)

Arguments

Value

a scalar of population-level sensitivity

Examples

# 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)

Specificity of tests in parallel

Description

Calculates the combined specificity for multiple tests interpreted in parallel (assuming independence)

Usage

sp.parallel(sp)

Arguments

Value

scalar of combined specificity, assuming independence

Examples

# examples for sp.parallel
sp.parallel(c(0.99, 0.95, 0.8))

Specficity of tests in series

Description

Calculates the combined specificity for multiple tests interpreted in series (assuming independence)

Usage

sp.series(sp)

Arguments

Value

scalar of combined specificity, assuming independence

Examples

# examples for sp.series
sp.series(c(0.99, 0.95, 0.8))

Binomial population specificity for imperfect test

Description

Calculates population specificity for a large or unknown population, using the Binomial distribution and adjusting for cut-point number of positives

Usage

sph.binom(n, c = 1, sp)

Arguments

Value

a vector of population-level specificities

Examples

# 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)

Hypergeometric population specificity calculation

Description

Calculates population specificity for a finite population and imperfect test, using Hypergeometric distribution

Usage

sph.hp(N, n, c = 1, sp)

Arguments

Value

a vector of population-level specificities

Examples

# 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)

Population specificity

Description

Calculates population specificity assuming representative sampling

Usage

spp(n, sp)

Arguments

Value

a vector of population-level specificities

Examples

# 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))

System sensitivity by combining multiple surveillance components

Description

Calculates overall system sensitivity for multiple components, accounting for lack of independence (overlap) between components

Usage

sse.combined(C = NA, pstar.c, rr, ppr, sep)

Arguments

Value

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

Examples

# 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)

Two-stage risk-based system sensitivity

Description

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

Usage

sse.rb.2stage(C = NA, pstar.c, pstar.u, rr.c, ppr.c, rr.u, ppr.u,
  N = NA, n, rg, se)

Arguments

Value

list of 2 elements, a scalar of population-level (surveillance system) sensitivity and a vector of cluster-level sensitivities

Examples

# 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) 

True prevalence

Description

Estimates true prevalence and confidence limits for given sample size and result, according to specified method

Usage

tp(x, n, se, sp, type = "blaker", conf = 0.95)

Arguments

Value

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

# 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")

Normal approximation confidence limits for true prevalence

Description

Estimates true prevalence and confidence limits for estimates based on normal approximation

Usage

tp.normal(x, n, se, sp, conf = 0.95)

Arguments

Value

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

# examples for tp.normal
tp.normal(25, 120, 0.9, 0.99)
tp.normal(seq(5, 25, by=5), 120, 0.9, 0.99)