Diagnosis Performance Using Attributable Fraction

Estimate diagnosis performance (Sensitivity, Specificity, Positive predictive value, Negative predicted value) of a diagnostic test where can not measure the golden standard but can estimate it using the attributable fraction.


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Diagnosis performance using attributable fraction

This R-package help on the estimation of diagnosis performance (Sensitivity, Specificity, Positive predictive value, Negative predicted value) of a diagnostic test where the golden standard can't be measured but can be estimated using the attributable fraction

Two methods are presented with examples for Malaria diagnosis, using a maximum likelihood estimated logistic exponential model and using a bayesian latent class model.

To install the package from github use:

devtools::install_github("johnaponte/afdx", build_manual = T, build_vignettes = T)

Reference manual

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install.packages("afdx")

1.1.2 by John J. Aponte, 7 months ago


https://github.com/johnaponte/afdx


Browse source code at https://github.com/cran/afdx


Authors: John J. Aponte [aut, cre] (ORCID: , Orvalho Augusto [aut]


Documentation:   PDF Manual  


GPL (>= 3) license


Imports maxLik, dplyr, magrittr, tidyr

Suggests knitr, rmarkdown, ggplot2, DescTools, kableExtra, coda, rjags, ggmcmc, spelling, testthat


See at CRAN