Operating characteristics for hierarchical group testing
Calculate operating characteristics for hierarchical group testing with up to four stages, given a vector of individual probabilities and a testing configuration.
hierarchical.desc2(p, I2 = NULL, I3 = NULL, se = 1, sp = 1, order.p = TRUE)
p |
vector of probabilities corresponding to each individual's risk of disease. |
I2 |
a numeric vector of pool sizes for stage 2 testing (used in hierarchical testing with at least three stages). |
I3 |
a numeric vector of pool sizes for stage 3 testing (used in hierarchical testing with at least four stages). |
se |
the sensitivity of the diagnostic test. |
sp |
the specificity of the diagnostic test. |
order.p |
a logical value indicating whether the vector of individual probabilities needs to be sorted. |
This function calculates the operating characteristics for hierarchical group testing with up to four stages of testing. Operating characteristics calculated are expected number of tests, and pooling sensitivity, pooling specificity, pooling positive predictive value, and pooling negative predictive value, for each individual and for the configuration overall.
If I2 is NULL, there are two stages of testing. If I3 is NULL but I2 has values, there are three stages of testing. If both I2 and I3 have values, there are four stages of testing.
Vectors I2 and I3 should be entered using notation that keeps track of all individuals through all stages (e.g. for a group of 10 individuals that splits into 5, 4, and 1 individual at stage 2, then into 3, 2, 2, 1, and 1 individual at stage 3 before individual testing at stage 4, then I2=c(5,4,1) and I3=c(3,2,2,1,1,1) so that the specimen that was tested individually at stage 2 is still numbered at stage 3 even though it will not be tested again).
The displayed pooling sensitivity, pooling specificity, pooling positive predictive value, and pooling negative predictive value are weighted averages of the corresponding individual accuracy measures for all individuals within the initial group for a hierarchical algorithm. Expressions for these averages are provided in the Supplementary Material for Hitt et al. (2018). These expressions are based on accuracy definitions given by Altman and Bland (1994a, 1994b).
A list containing:
ET |
the expected number of tests. |
stages |
the number of stages in the testing algorithm. |
group.size |
the total number of individuals tested in the algorithm. |
I2 |
pool sizes for the second stage of testing, or "individual testing" if there are only two stages of testing. |
I3 |
pool sizes for the third stage of testing, or "individual testing" if there are only three stages of testing. |
m1 |
the initial (stage 1) group size for two stage testing, or the number of subgroups originating from the initial group. |
m2 |
the number of subgroups for each preceding group containing more than one individual, or "individual testing" if there are only two stages of testing. |
m3 |
the number of subgroups for each preceding group containing more than one individual, or "individual testing" if there are only three stages of testing. NULL if there are only two stages of testing. |
individual.testerror |
a data frame containing:
|
group.testerror |
a vector containing:
|
individual.probabilities |
a vector containing each individual's probability of disease. If order.p=TRUE, this is the sorted vector of individual probabilities. |
This function returns the pooling positive and negative predictive values for all individuals even though these measures are diagnostic specific; i.e., PPPV (PNPV) should only be considered for those individuals who have tested positive (negative).
This function was originally written by Michael S. Black for Black et al. (2015). The function was obtained from http://chrisbilder.com/grouptesting. Minor modifications were made to the function for inclusion in the binGroup package.
Black, M., Bilder, C., Tebbs, J. (2015). “Optimal retesting configurations for hierarchical group testing.” Journal of the Royal Statistical Society. Series C: Applied Statistics, 64(4), 693–710. ISSN 14679876, doi: 10.1111/rssc.12097.
Array.Measures for calculating operating characteristics
under array testing without master pooling,
MasterPool.Array.Measures for non-informative array
testing with master pooling, and inf.dorf.measures
for informative two-stage hierarchical testing. See
p.vec.func for generating a vector of
individual risk probabilities for informative group testing.
Other Operating characteristic functions: Array.Measures,
MasterPool.Array.Measures,
inf.dorf.measures
# Calculate the operating characteristics for # non-informative two-stage hierarchical testing # with an overall disease prevalence of p = 0.015 # and an initial group size of 12. # This example takes less than 1 second to run. # Estimated running time was calculated using a # computer with 16 GB of RAM and one core of an # Intel i7-6500U processor. p.vec <- rep(x=0.015, times=12) hierarchical.desc2(p=p.vec, I2=NULL, I3=NULL, se=0.95, sp=0.95, order.p=FALSE) # Calculate the operating characteristics for # non-informative three-stage hierarchical testing # with an overall disease prevalence of p = 0.04, # where an initial group of 20 individuals is # split into equally sized subgroups of 5 each. # This example takes less than 1 second to run. # Estimated running time was calculated using a # computer with 16 GB of RAM and one core of an # Intel i7-6500U processor. p.vec <- rep(x=0.04, times=20) hierarchical.desc2(p=p.vec, I2=rep(x=5, times=4), I3=NULL, se=0.99, sp=0.99, order.p=FALSE) # Calculate the operating characteristics for # informative three-stage hierarchical testing # where an initial group of 10 individuals is # split into subsequent groups of 5, 4, and 1 # individual. # A vector of individual probabilities is generated using # the expected value of order statistics from a beta # distribution with p = 0.02 and a heterogeneity level # of alpha = 0.5. Depending on the specified probability, # alpha level, and overall group size, simulation may # be necessary in order to generate the vector of individual # probabilities. This is done using p.vec.func() and # requires the user to set a seed in order to reproduce # results. # This example takes less than 1 second to run. # Estimated running time was calculated using a # computer with 16 GB of RAM and one core of an # Intel i7-6500U processor. set.seed(1002) p.vec <- p.vec.func(p=0.02, alpha=0.5, grp.sz=10) hierarchical.desc2(p=p.vec, I2=c(5,4,1), I3=NULL, se=0.90, sp=0.90, order.p=TRUE)
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