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afdx: Diagnosis performance using attributable fraction

Introduction

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)

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Install

install.packages('afdx')

Monthly Downloads

232

Version

1.1.1

License

GPL (>= 3)

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Maintainer

Aponte John

Last Published

May 25th, 2021

Functions in afdx (1.1.1)

get_latent_model

Template for the bayesian latent class model
make_n_cutoffs

Make a defined number of categories having similar number of positives in each category
malaria_df1

Synthetic data simulating a malaria crossectional
afdx-package

afdx: Diagnosis performance indicators from attributable fraction estimates.
malaria_df2

Synthetic data simulating a malaria crossectional
senspec

S3 methods to estimate diagnosis performance of an afmodel
make_cutoffs

Cut-off points for densities and fever
logitexp

Exponential logit model for two variables