afdx

NEWS code{white-space: pre-wrap;} span.smallcaps{font-variant: small-caps;} span.underline{text-decoration: underline;} div.column{display: inline-block; vertical-align: top; width: 50%;} div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;} ul.task-list{list-style: none;} afdx 1.1.2 Documentation Update the NEWS.md to the correct formatting Add a logo Add a pkgdown site Fix outdated URLs DOI added to the references in the vignettes afdx 1.1.1 Documentation Improved documentation to include examples and tests. afdx 1.1.0 CRAN submission First attempt to submit the package to CRAN. afdx 0.4 Improvements First version of the vignette for logitexponential ( @johnaponte ). afdx 0.3 New features Inclusion of the Bayesian model ( @johnaponte ). afdx 0.2 New features Logitexponential model and associated functions ( @johnaponte ). afdx 0.1 Initial development Creation and documentation of synthetic data ( @johnaponte ).

README code{white-space: pre-wrap;} span.smallcaps{font-variant: small-caps;} span.underline{text-decoration: underline;} div.column{display: inline-block; vertical-align: top; width: 50%;} div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;} ul.task-list{list-style: none;} afdx 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)

Help for package afdx const macros = { "\\R": "\\textsf{R}", "\\mbox": "\\text", "\\code": "\\texttt"}; function processMathHTML() { var l = document.getElementsByClassName('reqn'); for (let e of l) { katex.render(e.textContent, e, { throwOnError: false, macros }); } return; } Package {afdx} Contents afdx-package get_latent_model logitexp make_cutoffs make_n_cutoffs malaria_df1 malaria_df2 senspec Title: Diagnosis Performance Using Attributable Fraction Version: 1.1.2 Date: 2026-03-06 URL: https://github.com/johnaponte/afdx Description: 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. License: GPL (≥ 3) Encoding: UTF-8 LazyData: true RoxygenNote: 7.3.3 Depends: R (≥ 3.5.0) Imports: maxLik, dplyr, magrittr, tidyr Suggests: knitr, rmarkdown, ggplot2, DescTools, kableExtra, coda, rjags, ggmcmc, spelling, testthat (≥ 3.0.0) VignetteBuilder: knitr Language: en-US Config/testthat/edition: 3 NeedsCompilation: no Packaged: 2026-03-06 13:09:46 UTC; master Author: John J. Aponte [aut, cre], Orvalho Augusto [aut] Maintainer: John J. Aponte <john.j.aponte@gmail.com> Repository: CRAN Date/Publication: 2026-03-06 13:30:15 UTC afdx: Diagnosis performance indicators from attributable fraction estimates. Description The afdx package provides functions to estimate the attributable fraction using logit exponential model or bayesian latent class model. The logit exponential model The logitexp function estimated the logit exponential function fitting a maximum likelihood model. The senspec() function estimate the sensitivity, specificity, positive predicted value and negative predicted values for the specified cut-off points. The bayesian latent class model The get_latent_model() provides an rjags model template to estimate the attributable fraction and the sensitivity, specificity, positive predicted value and negative predicted value of the latent class model. @docType package @name afdx Author(s) Maintainer : John J. Aponte john.j.aponte@gmail.com ( ORCID ) Authors: Orvalho Augusto caveman@gmail.com ( ORCID ) See Also Useful links: https://github.com/johnaponte/afdx Template for the bayesian latent class model Description This function returns a template that can be use as model in an rjags model it requires two vectors with the number of subjects in the symptoms, like fever in the case of malaria (n) and the number of non-symptomatic (m) in each of the categories of results of the diagnostic test. The first category is reserved for the negatives by the diagnostic test (in the malaria case those with asexual density 0) and the rest categories each one with higher values than the previous category. Usage get_latent_model() Details See: Smith T, Vounatsou P. Logistic regression and latent class models for estimating positivities in diagnostic assays with poor resolution. Communications in Statistics - Theory and Methods. 1997 Jan;26(7):1677–700. Vounatsou P, Smith T, Smith AFM. Bayesian analysis of two-component mixture distributions applied to estimating malaria attributable fractions. Journal of the Royal Statistical Society: Series C (Applied Statistics). 1998;47(4):575–87. Müller I, Genton B, Rare L, Kiniboro B, Kastens W, Zimmerman P, et al. Three different Plasmodium species show similar patterns of clinical tolerance of malaria infection. Malar J. 2009;8(1):158. Plucinski MM, Rogier E, Dimbu PR, Fortes F, Halsey ES, Aidoo M, et al. Performance of Antigen Concentration Thresholds for Attributing Fever to Malaria among Outpatients in Angola. J Clin Microbiol. 2019;57(3). Value a string value Examples { get_latent_model() } Exponential logit model for two variables Description Fit a logit model of v.density on v.fever v.density with a exponential coefficient for the v.density Usage logitexp(v.fever, v.density) Arguments v.fever numeric vector of 0/1 indicating fever or equivalent v.density numeric vector of values >= 0 indicating the density Details logit(v.fever) ~ beta * (v. density ^ tau) This corresponds to the model 3 describe by Smith, T., Schellenberg, J.A., Hayes, R., 1994. Attributable fraction estimates and case definitions for malaria in endemic areas. Stat Med 13, 2345–2358. Value S3 object of class afmodel with 4 components: data, model, coefficients and the estimated attributable fraction. See Also senspec Examples { # Get the sample data head(malaria_df1) fit <- logitexp(malaria_df1$fever, malaria_df1$density) fit senspec(fit, c(1,100,500,1000,2000,4000,8000,16000, 32000,54000,100000)) } Cut-off points for densities and fever Description Generate the cutoffs at every change of density in the fever, but first category is for density 0, and last category if possible have no subjects with no fever. Usage make_cutoffs(v.fever, v.density, add1 = TRUE) Arguments v.fever numeric vector of 0/1 indicating fever or equivalent v.density numeric vector of values >= 0 indicating the density add1 a logical value to indicate the category started with 1 is included Value a vector with the cutoff points Examples { make_cutoffs(malaria_df1$fever, malaria_df1$density, add1 = TRUE) } Make a defined number of categories having similar number of positives in each category Description Generate the categories in a way that each category have at least the mintot number of observation. It generate all possible categories were there is change and then collapse to have minimum number of observations in each category Usage make_n_cutoffs(v.fever, v.density, mintot, add1 = TRUE) Arguments v.fever numeric vector of 0/1 indicating fever or equivalent v.density numeric vector of values >= 0 indicating the density mintot minimum number of observations per category add1 a logical value to indicate the category started with 1 is included Value a vector with the cutoff points Examples { make_n_cutoffs(malaria_df1$fever, malaria_df1$density, mintot=50) } Synthetic data simulating a malaria crossectional Description Simulated data with the main outcomes of a malaria crossectional, fever and parasite density Usage malaria_df1 Format a dataset with two variables fever 1 if fever or history of fever, 0 otherwise density asexual Plasmodium parasite density, in parasites per ul Synthetic data simulating a malaria crossectional Description Simulated data with the main outcomes of a malaria crossectional, fever and parasite density Usage malaria_df2 Format a dataset with two variables fever 1 if fever or history of fever, 0 otherwise density asexual Plasmodium parasite density, in parasites per ul S3 methods to estimate diagnosis performance of an afmodel Description Estimate sensitivity, specificity, positive predicted value and negative predicted value negative predictive value from an afmodel. The estimated "true" negative and "true" positive are estimated using the estimated overall attributable fraction and the predictive positive value associated with each cut-off point as described by Smith, T., Schellenberg, J.A., Hayes, R., 1994. Attributable fraction estimates and case definitions for malaria in endemic areas. Stat Med 13, 2345–2358. Usage senspec(object, ...) ## Default S3 method: senspec(object, ...) ## S3 method for class 'afmodel' senspec(object, cutoff, ...) Arguments object with the data to calculate the sensitivity and specificity ... other parameters for the implementing functions cutoff vector of cut-off points to make the estimations Value a matrix with the columns sensitivity and specificity, ppv (positive predicted value) and npv (negative predicted value) No return value. Raise an error. a matrix with the columns sensitivity and specificity, ppv (positive predicted value) and npv (negative predicted value) See Also logitexp Examples { # Get the sample data head(malaria_df1) fit <- logitexp(malaria_df1$fever, malaria_df1$density) fit senspec(fit, c(1,100,500,1000,2000,4000,8000,16000