In lshtm-vimc/prime: PRIME -- Papillomavirus Rapid Interface for Modelling and Economics

# load package - prime
library(prime)

PRIME

PRIME stands for Papillomavirus Rapid Interface for Modelling and Economics. The R package (prime) is based of the spreadsheet-based tool (see http://primetool.org).

PRIME provides users with an idea of:

• The magnitude of the burden of cervical cancer
• The impact of introducing HPV vaccination for girls prior to sexual debut
• Healthcare costs incurred as a result of cervical cancer treatment
• Costs associated with vaccination
• Long-term savings which may result from a vaccination program

Functions

The main PRIME functions are:

• RunCohort
• RunCountry
• analyseCosts

Other functions include:

• writelog
• RegisterBatchData
• RegisterBatchDataGavi
• BatchRun
• OutputGavi
• getISO3
• checkSize
• lifeTable
• ageCoverage
• propSexDebut
• dtColMatch
• dtAggregate
• monetary_to_number

R code for the functions are in the R-folder of the package, i.e. ./R/

Documentation of a function can be viewed, by running ?functionname in the console, i.e. ?RunCohort

# View documentation of RunCohort function
?RunCohort

The source code of a function can be viewed by running the functionname without any brackets in the console.

# View source code of RunCohort function
RunCohort

Data

Several data sets are included in the package. The data files (.rda files) are stored as data tables (data.table) in the data folder of the package, i.e. ./data/

• data.cecx_1y_prevalence
• data.cecx_3y_prevalence
• data.cecx_5y_prevalence
• data.costcecx
• data.countryname
• data.global
• data.incidence
• data.mortall
• data.mortall.unwpp.mx
• data.mortcecx
• data.pop
• data.popproj
• data.quality
• data.sexual_debut
• data.valid

Documentation of a data set can be viewed, by running ?dataset-name in the console, i.e. ?data.incidence

# View documentation of data.incidence data set
?data.incidence

A data set can be viewed by typing the data file name in the console.

# View data set for age-specific incidence of cervical cancer in different countries (estimated by Globocan)
data.incidence

Examples

Use RunCountry to run PRIME for a single birthcohort in a single country.

• Countries are referenced by their ISO3 code.

RunCountry ("RWA")

• This returns the age-specific rates for a country, given the values passed to RunCountry.
• The size of the cohort at each age is given in the cohort_size column.
• In order to get the number of people rather than the rates, the columns can be multiplied with the cohort_size.

result <- RunCountry ("RWA")
result [,c("vaccinated", "immunized", "inc.cecx", "mort.cecx", "lifey","disability", "cost.cecx")] <- result[, cohort_size] * result[,c("vaccinated", "immunized", "inc.cecx", "mort.cecx", "lifey", "disability", "cost.cecx")]
result [age>15]

If you don't know the exact ISO3 code for a country, you can try to look it up using the getISO3 function.

getISO3 ("Malawi")

If you want to analyse the costs/benefits of vaccinating this specific cohort, set analyseCosts to TRUE.

# To be updated
# RunCountry ("MWI", analyseCosts=TRUE)

RunCountry automatically assumes 100% coverage, 100% vaccine-efficacy, and age of vaccination at 12.

• If no year is provided, it will assume that vaccination will occur this year. Year can be provided as year-of-vaccination or birthcohort-year (i.e. year in which girls of this birth-cohort was born).
• Tweak the different values by passing them to the function.
• See ?RunCountry for all options.

# To be updated
# RunCountry("MWI", analyseCosts=TRUE, discounting=TRUE, agevac=10, agecohort=10, vaceff_beforesexdebut=0.98, vaceff_aftersexdebut=0, cov=0.75, year_vac=2020)

• In essence, RunCountry() calls RunCohort(), and will provide it with country-specific estimates.
• When analyseCosts is set to TRUE, RunCountry() calls analyseCosts() and passes the results of RunCohort().
• You could also run it yourself, providing your own estimates for vaccine-costs and GDP per capita.

# To be updated
# analyseCosts (
#   RunCountry("MWI", analyseCosts=FALSE, discounting=TRUE, agevac=10, agecohort=10, vaceff_beforesexdebut=0.98, vaceff_aftersexdebut=0, cov=0.75, year_vac=2020),
#   vaccine_cost=100,
#   gdp_per_capita=561
# )

• Lifetables are used to follow the size of a birth-cohort over time.
• They are generated based on values for the qx-column (i.e. probability of dying by age).
• The lifeTable() function makes it easy to generate a lifetable.

qx <- unlist(data.mortall[iso3=="MWI", as.character(0:100), with=F], use.names=F)
lifeTable(qx)

• RunCohort() uses this lifetable. It calculates age-specific coverage-rates using the ageCoverage() function.

ages <- c(0:100)
routine_coverage <- 0.75
vaccine_efficacy_nosexdebut <- 0.99
vaccine_efficacy_sexdebut <- 0
lifetab <- lifeTable(unlist(data.mortall[iso3=="MWI", as.character(0:100), with=F], use.names=F), 9)
cohort <- unlist(data.popproj[iso3=="MWI", "2020"], use.names=F)
agevac <- 9
ageCoverage(ages, routine_coverage, vaccine_efficacy_nosexdebut=1, vaccine_efficacy_sexdebut=0, -1, lifetab, cohort, agevac, country_iso3="MWI")

• Data can be aggregated using the dtAggregate() function.

result <- RunCountry ("MWI", analyseCosts=FALSE, discounting=TRUE, agevac=10, agecohort=10, vaceff_beforesexdebut=0.98, vaceff_aftersexdebut=0, cov=0.75, year_vac=2020)

result [,c("vaccinated", "immunized", "inc.cecx", "mort.cecx", "lifey", "disability", "cost.cecx")] <- result [, cohort_size] * result[,c("vaccinated", "immunized", "inc.cecx", "mort.cecx", "lifey", "disability", "cost.cecx")]

# aggregate results on age
result <- dtAggregate (result, "age", id.vars=c("scenario", "type") )
result

lshtm-vimc/prime documentation built on April 21, 2024, 3:21 a.m.