On the robustness of latent class models for diagnostic testing with no gold standard. (14th May 2021)

• Record Type: Journal Article
• Authors: Schofield, Matthew R.; Maze, Michael J.; Crump, John A.; Rubach, Matthew P.; Galloway, Renee; Sharples, Katrina J.
• Is Part Of: Statistics in medicine. Volume 40:Number 22(2021)
• Page Start: 4751
• Page End: 4763
• Publication Date: 2021-05-14
• Subjects: Bayes -- leptospirosis -- model sensitivity -- sensitivity -- specificity; Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.8999
• Languages: English
• ISSNs: 0277-6715

Abstract

Abstract : It is difficult to estimate sensitivity and specificity of diagnostic tests when there is no gold standard. Latent class models have been proposed as a potential solution as they provide estimates without the need for a gold standard. Using a motivating example of the evaluation of point of care tests for leptospirosis in Tanzania, we show how a realistic violation of assumptions underpinning the latent class model can lead directly to substantial bias in the estimates of the parameters of interest. In particular, we consider the robustness of estimates of sensitivity, specificity, and prevalence, to the presence of additional latent states when fitting a two‐state latent class model. The violation is minor in the sense that it cannot be routinely detected with goodness‐of‐fit procedures, but is major with regard to the resulting bias.