rsu.sep.rb | R Documentation |
Calculates surveillance system (population-level) sensitivity assuming one-stage, risk-based sampling and varying unit sensitivity using either the binomial or hypergeometric methods.
rsu.sep.rb(N, rr, ppr, df, pstar, method = "binomial")
N |
vector of the same length as |
rr |
vector of length equal to the number of risk strata, the relative risk values. |
ppr |
vector of the same length as |
df |
a dataframe of values for each combination of risk stratum and sensitivity level. Column 1 = risk group index, column 2 = unit sensitivities, column 3 = the sample size for risk group and unit sensitivity). |
pstar |
scalar, the design prevalence. |
method |
character string indicating the method to be used. Options are |
If method = binomial
N
is ignored and values for ppr
need to be entered. Conversely, if method = hypergeometric
, ppr
is ignored and calculated from N
.
A list comprised of five elements:
sep |
scalar, the population-level sensitivity estimate. |
epi |
vector, effective probability of infection estimates. |
adj.risk |
vector, adjusted risks. |
n |
vector, sample size by risk group |
se.u |
a vector of the mean sensitivity for each risk group. |
## EXAMPLE 1:
## Calculate the surveillance system sensitivity assuming one-stage risk-
## based sampling assuming a population comprised of high risk (n = 200
## clusters) and low risk (n = 1800 clusters) where the probability of
## disease in the high risk group is 5 times that of the low risk group.
## Four clusters will be sampled with n = 80, 30, 20 and 30 surveillance
## units within each cluster tested using a test with diagnostic sensitivity
## at the surveillance unit level of 0.92, 0.85, 0.92 and 0.85, respectively.
## Assume a design prevalence of 0.01.
rg <- c(1,1,2,2)
se.u <- c(0.92,0.85,0.92,0.85)
n <- c(80,30,20,30)
df <- data.frame(rg, se.u, n)
rsu.sep.rb(N = c(200,1800), rr = c(5,1), ppr = NA, df = df, pstar = 0.01,
method = "hypergeometric")
## The expected surveillance system sensitivity is 0.993.
## EXAMPLE 2:
## Recalculate, assuming that we don't know the size of the cluster population
## at risk.
## When the size of the cluster population at risk is unknown we set N = NA
## and enter values for ppr (the proportion of the population in each risk
## group). Assume (from above) that 0.10 of the cluster population are in the
## high risk group and 0.90 are in the low risk group.
rsu.sep.rb(N = NA, rr = c(5,1), ppr = c(0.10,0.90), df = df, pstar = 0.01,
method = "binomial")
## The expected surveillance system sensitivity is 0.980.
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