## EXAMPLE 1:
## You are presented with a dog with lethargy, exercise intolerance,
## weight gain and bilaterally symmetric truncal alopecia. You are
## suspicious of hypothyroidism and take a blood sample to measure
## basal serum thyroxine (T4).
## You believe that around 5% of dogs presented to your clinic with
## a signalment of general debility have hypothyroidism. The serum T4
## has a sensitivity of 0.89 and specificity of 0.85 for diagnosing
## hypothyroidism in the dog. The laboratory reports a serum T4
## concentration of 22.0 nmol/L (reference range 19.0 to 58.0 nmol/L).
## What is the post-test probability that this dog is hypothyroid?
epi.nomogram(se = 0.89, sp = 0.85, lr = NA, pre.pos = 0.05, verbose = FALSE)
## If the test is positive the post-test probability that this dog is
## hypothyroid is 0.24. If the test is negative the post-test probability
## that this dog is hypothyroid is 0.0068.
## EXAMPLE 2:
## A dog is presented to you with severe pruritis. You suspect sarcoptic
## mange and decide to take a skin scraping (LR+ 9000; LR- 0.1). The scrape
## returns a negative result (no mites are seen). What is the post-test
## probability that your patient has sarcoptic mange? You recall that you
## diagnose around 3 cases of sarcoptic mange per year in a clinic that
## sees approximately 2 -- 3 dogs per week presented with pruritic skin disease.
## Calculate the pre-test probability of sarcoptes:
pre.pos <- 3 / (3 * 52)
## The pre-test probability that this dog is sarcoptes positive is 0.019.
epi.nomogram(se = NA, sp = NA, lr = c(9000, 0.1), pre.pos = pre.pos,
verbose = FALSE)
## If the skin scraping is negative the post-test probability that this dog
## has sarcoptic mange is 0.002.
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