epi.nomogram {epiR} | R Documentation |

Compute the post-test probability of disease given sensitivity and specificity of a test.

epi.nomogram(se, sp, lr, pre.pos, verbose = FALSE)

`se` |
test sensitivity (0 - 1). |

`sp` |
test specificity (0 - 1). |

`lr` |
a vector of length 2 listing the positive and negative likelihood ratio (respectively) of the test. Ignored if |

`pre.pos` |
the pre-test probability of the outcome. |

`verbose` |
logical, indicating whether detailed or summary results are to be returned. |

A list containing the following:

`lr` |
a data frame listing the likelihood ratio of a positive and negative test. |

`prior` |
a data frame listing the pre-test probability of being outcome (i.e. disease) positive, as entered by the user. |

`post` |
a data frame listing: |

Caraguel C, Vanderstichel R (2013). The two-step Fagan's nomogram: ad hoc interpretation of a diagnostic test result without calculation. Evidence Based Medicine 18: 125 - 128.

Hunink M, Glasziou P (2001). Decision Making in Health and Medicine - Integrating Evidence and Values. Cambridge University Press, pp. 128 - 156.

## 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.

[Package *epiR* version 2.0.38 Index]