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This document describes how to create a population class object using PoDBAY package. Population class is used across the vignettes and package functions (see vignette("efficacyestimation", package = "PoDBAY") and vignette("simulation", package = "PoDBAY")) and it allows the user utilize the different populations, e.g. vaccinated, control, diseased, nondiseased.
For creation of any population class object generatePopulation function is used. The goal of this document is to show:
The easiest application of the population class is to generate an empty population class object i.e. population class object with no characteristics which can later be adjusted.
populationEmpty <- generatePopulation()
populationEmpty
#> Reference class object of class "Population"
#> Field "N":
#> [1] 0
#> Field "mean":
#> [1] NA
#> Field "stdDev":
#> [1] NA
#> Field "unknownDistribution":
#> [1] FALSE
#> Field "UDFunction":
#> function ()
#> NULL
#> <bytecode: 0x24d3840>
#> Field "titers":
#> numeric(0)
#> Field "PoDs":
#> numeric(0)
#> Field "diseaseStatus":
#> logical(0)Sometimes we want to generate a population class object based on the summary statistics (N, mean, sd). For example, this approach can be used when creating population class objects in the simulations - see vignette("simulation", package = "PoDBAY") for more details.
In this example we create two population class objects - vaccinated and control.
Approach 1 This approach is useful when we don’t necessarily need to generate individual titers.
vaccinated <- generatePopulation()
vaccinated$N <- 1000
vaccinated$mean <- 7
vaccinated$stdDev <- 2
str(vaccinated)
#> Reference class 'Population' [package "PoDBAY"] with 8 fields
#> $ N : num 1000
#> $ mean : num 7
#> $ stdDev : num 2
#> $ unknownDistribution: logi FALSE
#> $ UDFunction :function ()
#> $ titers : num(0)
#> $ PoDs : num(0)
#> $ diseaseStatus : logi(0)
#> and 24 methods, of which 10 are possibly relevant:
#> assignPoD, getDiseasedCount, getDiseasedTiters, getNondiseasedCount,
#> getNondiseasedTiters, getTiters, getUnknown, initialize, popFun, popX
control <- generatePopulation()
control$N <- 1000
control$mean <- 5
control$stdDev <- 2
str(control)
#> Reference class 'Population' [package "PoDBAY"] with 8 fields
#> $ N : num 1000
#> $ mean : num 5
#> $ stdDev : num 2
#> $ unknownDistribution: logi FALSE
#> $ UDFunction :function ()
#> $ titers : num(0)
#> $ PoDs : num(0)
#> $ diseaseStatus : logi(0)
#> and 24 methods, of which 10 are possibly relevant:
#> assignPoD, getDiseasedCount, getDiseasedTiters, getNondiseasedCount,
#> getNondiseasedTiters, getTiters, getUnknown, initialize, popFun, popXApproach 2 If the population summary statistics parameters are used as function input individual titers are generated as well (by sampling from normal distribution). These titers can be later changed if needed.
vaccinated <- generatePopulation(N = 1000,
mean = 7,
stdDev = 2)
str(vaccinated)
#> Reference class 'Population' [package "PoDBAY"] with 8 fields
#> $ N : num 1000
#> $ mean : num 7
#> $ stdDev : num 2
#> $ unknownDistribution: logi FALSE
#> $ UDFunction :function ()
#> $ titers : num [1:1000] 5.75 7.37 5.33 10.19 7.66 ...
#> $ PoDs : num(0)
#> $ diseaseStatus : logi(0)
#> and 24 methods, of which 10 are possibly relevant:
#> assignPoD, getDiseasedCount, getDiseasedTiters, getNondiseasedCount,
#> getNondiseasedTiters, getTiters, getUnknown, initialize, popFun, popX
control <- generatePopulation(N = 1000,
mean = 5,
stdDev = 2)
str(control)
#> Reference class 'Population' [package "PoDBAY"] with 8 fields
#> $ N : num 1000
#> $ mean : num 5
#> $ stdDev : num 2
#> $ unknownDistribution: logi FALSE
#> $ UDFunction :function ()
#> $ titers : num [1:1000] 7.27 7.22 3.26 5.42 5.14 ...
#> $ PoDs : num(0)
#> $ diseaseStatus : logi(0)
#> and 24 methods, of which 10 are possibly relevant:
#> assignPoD, getDiseasedCount, getDiseasedTiters, getNondiseasedCount,
#> getNondiseasedTiters, getTiters, getUnknown, initialize, popFun, popXThe advantage of the population class is that we can apply certain predefined class methods/functions. This simplifies working with population data (in the form of population class object) substantially.
list of useful available methods:
Let’s start with the two population class objects - vaccinated, control - defined above.
vaccinated <- generatePopulation()
vaccinated$N <- 1000
vaccinated$mean <- 7
vaccinated$stdDev <- 2
str(vaccinated)
#> Reference class 'Population' [package "PoDBAY"] with 8 fields
#> $ N : num 1000
#> $ mean : num 7
#> $ stdDev : num 2
#> $ unknownDistribution: logi FALSE
#> $ UDFunction :function ()
#> $ titers : num(0)
#> $ PoDs : num(0)
#> $ diseaseStatus : logi(0)
#> and 24 methods, of which 10 are possibly relevant:
#> assignPoD, getDiseasedCount, getDiseasedTiters, getNondiseasedCount,
#> getNondiseasedTiters, getTiters, getUnknown, initialize, popFun, popX
control <- generatePopulation()
control$N <- 1000
control$mean <- 5
control$stdDev <- 2
str(control)
#> Reference class 'Population' [package "PoDBAY"] with 8 fields
#> $ N : num 1000
#> $ mean : num 5
#> $ stdDev : num 2
#> $ unknownDistribution: logi FALSE
#> $ UDFunction :function ()
#> $ titers : num(0)
#> $ PoDs : num(0)
#> $ diseaseStatus : logi(0)
#> and 24 methods, of which 10 are possibly relevant:
#> assignPoD, getDiseasedCount, getDiseasedTiters, getNondiseasedCount,
#> getNondiseasedTiters, getTiters, getUnknown, initialize, popFun, popXGenerate titers based on the population parameters - N, mean, stdDev - by sampling from the normal distribution.
str(vaccinated)
#> Reference class 'Population' [package "PoDBAY"] with 8 fields
#> $ N : num 1000
#> $ mean : num 7
#> $ stdDev : num 2
#> $ unknownDistribution: logi FALSE
#> $ UDFunction :function ()
#> $ titers : num [1:1000] 5.23 3.16 10.24 8.04 6.89 ...
#> $ PoDs : num(0)
#> $ diseaseStatus : logi(0)
#> and 24 methods, of which 10 are possibly relevant:
#> assignPoD, getDiseasedCount, getDiseasedTiters, getNondiseasedCount,
#> getNondiseasedTiters, getTiters, getUnknown, initialize, popFun, popX
str(control)
#> Reference class 'Population' [package "PoDBAY"] with 8 fields
#> $ N : num 1000
#> $ mean : num 5
#> $ stdDev : num 2
#> $ unknownDistribution: logi FALSE
#> $ UDFunction :function ()
#> $ titers : num [1:1000] 6.48 5.77 7.59 3.39 1.79 ...
#> $ PoDs : num(0)
#> $ diseaseStatus : logi(0)
#> and 24 methods, of which 10 are possibly relevant:
#> assignPoD, getDiseasedCount, getDiseasedTiters, getNondiseasedCount,
#> getNondiseasedTiters, getTiters, getUnknown, initialize, popFun, popXAssign PoD to each subject in the population based on the provided PoD function. We will use example from vignette("simulation", package = "PoDBAY").
# Define PoD curve parameters
PoDParams <- data.frame("pmax" = 0.05, "et50" = 5, "slope" = 7)
# Assign PoD
vaccinated$assignPoD(
PoD(titer = vaccinated$titers,
pmax = PoDParams$pmax,
et50 = PoDParams$et50,
slope = PoDParams$slope,
adjustTiters = FALSE
))
control$assignPoD(
PoD(titer = control$titers,
pmax = PoDParams$pmax,
et50 = PoDParams$et50,
slope = PoDParams$slope,
adjustTiters = FALSE
))
str(vaccinated)
#> Reference class 'Population' [package "PoDBAY"] with 8 fields
#> $ N : num 1000
#> $ mean : num 7
#> $ stdDev : num 2
#> $ unknownDistribution: logi FALSE
#> $ UDFunction :function ()
#> $ titers : num [1:1000] 5.23 3.16 10.24 8.04 6.89 ...
#> $ PoDs : num [1:1000] 0.021135 0.048083 0.000329 0.001738 0.004799 ...
#> $ diseaseStatus : logi(0)
#> and 24 methods, of which 10 are possibly relevant:
#> assignPoD, getDiseasedCount, getDiseasedTiters, getNondiseasedCount,
#> getNondiseasedTiters, getTiters, getUnknown, initialize, popFun, popX
str(control)
#> Reference class 'Population' [package "PoDBAY"] with 8 fields
#> $ N : num 1000
#> $ mean : num 5
#> $ stdDev : num 2
#> $ unknownDistribution: logi FALSE
#> $ UDFunction :function ()
#> $ titers : num [1:1000] 6.48 5.77 7.59 3.39 1.79 ...
#> $ PoDs : num [1:1000] 0.00701 0.01338 0.00255 0.04689 0.04996 ...
#> $ diseaseStatus : logi(0)
#> and 24 methods, of which 10 are possibly relevant:
#> assignPoD, getDiseasedCount, getDiseasedTiters, getNondiseasedCount,
#> getNondiseasedTiters, getTiters, getUnknown, initialize, popFun, popXAnother way to assign PoD
For using these methods, the population class object needs to have disease status assigned.
To assign disease status we will use ClinicalTrial function form the PoDBAY package.
CaseCount <- ClinicalTrial(vaccinated, control, CI = 0.95)
str(vaccinated)
#> Reference class 'Population' [package "PoDBAY"] with 8 fields
#> $ N : num 1000
#> $ mean : num 7
#> $ stdDev : num 2
#> $ unknownDistribution: logi FALSE
#> $ UDFunction :function ()
#> $ titers : num [1:1000] 5.23 3.16 10.24 8.04 6.89 ...
#> $ PoDs : num [1:1000] 0.021135 0.048083 0.000329 0.001738 0.004799 ...
#> $ diseaseStatus : logi [1:1000] FALSE FALSE FALSE FALSE FALSE FALSE ...
#> and 24 methods, of which 10 are possibly relevant:
#> assignPoD, getDiseasedCount, getDiseasedTiters, getNondiseasedCount,
#> getNondiseasedTiters, getTiters, getUnknown, initialize, popFun, popX
str(control)
#> Reference class 'Population' [package "PoDBAY"] with 8 fields
#> $ N : num 1000
#> $ mean : num 5
#> $ stdDev : num 2
#> $ unknownDistribution: logi FALSE
#> $ UDFunction :function ()
#> $ titers : num [1:1000] 6.48 5.77 7.59 3.39 1.79 ...
#> $ PoDs : num [1:1000] 0.00701 0.01338 0.00255 0.04689 0.04996 ...
#> $ diseaseStatus : logi [1:1000] FALSE TRUE FALSE FALSE FALSE FALSE ...
#> and 24 methods, of which 10 are possibly relevant:
#> assignPoD, getDiseasedCount, getDiseasedTiters, getNondiseasedCount,
#> getNondiseasedTiters, getTiters, getUnknown, initialize, popFun, popXvaccinated$getDiseasedCount()
#> [1] 9
vaccinated$getNondiseasedCount()
#> [1] 991
vaccinated$getDiseasedTiters()
#> [1] 3.291161 5.083290 5.032390 3.605223 7.581768 4.333546 3.962850 5.538260
#> [9] 4.949354
as_tibble(vaccinated$getNondiseasedTiters())
#> # A tibble: 991 x 1
#> value
#> <dbl>
#> 1 5.23
#> 2 3.16
#> 3 10.2
#> 4 8.04
#> 5 6.89
#> 6 8.39
#> 7 7.11
#> 8 4.38
#> 9 2.75
#> 10 6.58
#> # … with 981 more rowsWe use mock-up data from a clinical trial and show how to convert them into a population class object so the data can be applied in the PoDBAY package.
Assume we have 20 patients from which 10 are vaccinated and 10 are in the control group. For each patient we have measurement of their \(log_2(titer)\) values.
dataTrial <- data.frame("patno" = 1:20,
"treatment" = c(rep(FALSE, 10), rep(TRUE, 10)),
"titers" = as.numeric(c(rnorm(10, 5, 2),
rnorm(10, 7, 2)))
)
dataTrial
#> patno treatment titers
#> 1 1 FALSE 1.967253
#> 2 2 FALSE 6.258282
#> 3 3 FALSE 1.643612
#> 4 4 FALSE 7.359562
#> 5 5 FALSE 7.235309
#> 6 6 FALSE 2.524528
#> 7 7 FALSE 2.539671
#> 8 8 FALSE 6.195582
#> 9 9 FALSE 5.597729
#> 10 10 FALSE 4.779721
#> 11 11 TRUE 5.384650
#> 12 12 TRUE 7.229078
#> 13 13 TRUE 6.640960
#> 14 14 TRUE 7.109690
#> 15 15 TRUE 9.598280
#> 16 16 TRUE 6.130878
#> 17 17 TRUE 5.382154
#> 18 18 TRUE 5.955297
#> 19 19 TRUE 6.424569
#> 20 20 TRUE 5.713573We use approach from above and convert our mock-up data into the PoDBAY population class object.
# vaccinated
vacc <- dataTrial %>% filter(treatment)
vaccinated <- generatePopulation()
vaccinated$N <- nrow(vacc)
vaccinated$mean <- mean(vacc$titers)
vaccinated$stdDev <- sd(vacc$titers)
vaccinated$titers <- vacc$titers
vaccinated
#> Reference class object of class "Population"
#> Field "N":
#> [1] 10
#> Field "mean":
#> [1] 6.556913
#> Field "stdDev":
#> [1] 1.249035
#> Field "unknownDistribution":
#> [1] FALSE
#> Field "UDFunction":
#> function ()
#> NULL
#> <bytecode: 0x24d3840>
#> Field "titers":
#> [1] 5.384650 7.229078 6.640960 7.109690 9.598280 6.130878 5.382154 5.955297
#> [9] 6.424569 5.713573
#> Field "PoDs":
#> numeric(0)
#> Field "diseaseStatus":
#> logical(0)
# control
ctrl <- dataTrial %>% filter(!treatment)
control <- generatePopulation()
control$N <- nrow(ctrl)
control$mean <- mean(ctrl$titers)
control$stdDev <- sd(ctrl$titers)
control$titers <- ctrl$titers
control
#> Reference class object of class "Population"
#> Field "N":
#> [1] 10
#> Field "mean":
#> [1] 4.610125
#> Field "stdDev":
#> [1] 2.238717
#> Field "unknownDistribution":
#> [1] FALSE
#> Field "UDFunction":
#> function ()
#> NULL
#> <bytecode: 0x24d3840>
#> Field "titers":
#> [1] 1.967253 6.258282 1.643612 7.359562 7.235309 2.524528 2.539671 6.195582
#> [9] 5.597729 4.779721
#> Field "PoDs":
#> numeric(0)
#> Field "diseaseStatus":
#> logical(0)These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.