An experiment in how case rates change

case_rate_experiment.r

# How do case rates vary as sensitivity changes, given
# fixed population
# fixed % positive
# fixed number of cases? 


# Strings ain't factors
options(stringsAsFactors = FALSE)



library(ggplot2)
library(reshape2)


# Set up a sample dataset
x <- data.frame(n = 1000,
                n.pos = 50,
                n.neg = 950,
                cases = 10,
                pos.sens = seq(.5, 1, length.out = 100)

)

# Calculate the number of cases falling into each group
x$pos.cases <- with(x, cases * pos.risk)
x$neg.cases <- with(x, cases * (1 - pos.risk))

# Corresponding rates per 100k
x$overall.rate <- with(x, (cases / n) * 1e5)
x$pos.rate <- with(x, (pos.cases / n.pos) * 1e5)
x$neg.rate <- with(x, (neg.cases / n.neg) * 1e5)


# Melt it down for ggplot
xplot <- melt(x, measure.vars = c("overall.rate", "pos.rate", "neg.rate"))

ggplot(xplot, aes(x = pos.risk, y = value, 
                  group = variable, color = variable)) +
    geom_line(size = 1.5) +
    scale_color_discrete("Rate Groups") +
    labs(x = "Proportion of cases who tested positive",
         y = "Cases per 100,000 dudes")