AI Article Synopsis
- Tests can yield false positives and false negatives, affecting the accuracy of disease prevalence estimates; thus, methods that adjust for the sensitivity and specificity of diagnostic tests are necessary.
- A web-based application called |tPRiors| has been developed to help researchers apply Bayesian inference methods for true disease prevalence estimation, featuring an interactive user interface that simplifies analysis.
- The tool allows for analysis of both preloaded and user-uploaded datasets, providing visualizations and reports to aid researchers in understanding and applying complex statistical methods more easily.
Article Abstract
Background: Tests have false positive or false negative results, which, if not properly accounted for, may provide misleading apparent prevalence estimates based on the observed rate of positive tests and not the true disease prevalence estimates. Methods to estimate the true prevalence of disease, adjusting for the sensitivity and the specificity of the diagnostic tests are available and can be applied, though, such procedures can be cumbersome to researchers with or without a solid statistical background. This manuscript introduces a web-based application that integrates statistical methods for Bayesian inference of true disease prevalence based on prior elicitation for the accuracy of the diagnostic tests. This tool allows practitioners to simultaneously analyse and visualize results while using interactive sliders and output prior/posterior plots.
Methods - Implementation: Three methods for prevalence prior elicitation and four core families of Bayesian methods have been combined and incorporated in this web tool. |tPRiors| user interface has been developed with R and Shiny and may be freely accessed on-line.
Results: |tPRiors| allows researchers to use preloaded data or upload their own datasets and perform analysis on either single or multiple population groups clusters, allowing, if needed, for excess zero prevalence. The final report is exported in raw parts either as.rdata or.png files and can be further analysed. We utilize a real multiple-population and a toy single-population dataset to demonstrate the robustness and capabilities of |tPRiors|.
Conclusions: We expect |tPRiors| to be helpful for researchers interested in true disease prevalence estimation and who are keen on accounting for prior information. |tPRiors| acts both as a statistical tool and a simplified step-by-step statistical framework that facilitates the use of complex Bayesian methods. The application of |tPRiors| is expected to aid standardization of practices in the field of Bayesian modelling on subject and multiple group-based true prevalence estimation.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8977049 | PMC |
| http://dx.doi.org/10.1186/s12874-022-01557-1 | DOI Listing |
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