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On the robustness of latent class models for diagnostic testing with no gold standard
Author(s) -
Schofield Matthew R.,
Maze Michael J.,
Crump John A.,
Rubach Matthew P.,
Galloway Renee,
Sharples Katrina J.
Publication year - 2021
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.8999
Subject(s) - latent class model , robustness (evolution) , econometrics , gold standard (test) , statistics , sensitivity (control systems) , goodness of fit , computer science , class (philosophy) , mathematics , artificial intelligence , gene , biochemistry , chemistry , electronic engineering , engineering
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.