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Several studies utilize multiple diagnostic models.
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LCA fits a model to data from multiple diagnostic tests to model the prevalence of a latent class (disease status) in the analyzed population.
Because the time points are unevenly spaced, we could have fitted transition hazards rather than transition probabilities (in other words, hidden Markov models), but then only 1 diagnostic test would be evaluated because it is not computationally tractable to fit such models to multiple diagnostic tests simultaneously.
In this study, we provide additional characterization of the TH-MYCN mouse model using multiple diagnostic imaging methodologies and gene profiling.
LCA modeling can assess multiple diagnostic tests simultaneously and has been used to evaluate tests for other infections with imperfect reference standards [ 13, 14].
Future work would be to increase the complexity of the model to incorporate re-assessments and multiple diagnostic pathways.
However, in cases of less evenly spaced time points when simultaneous evaluation of multiple diagnostic tests with measurement error is not of primary interest, hidden Markov models might be more appropriate as a method of analysis.
Results must also be used with caution as the hospital ED model does not incorporate the entire patient flow or possible variations in routing (e.g. multiple diagnostic tests and re-assessments) due to data limitations.
She described the mental-health system as dogmatic and overly attached to its diagnostic models.
Multiple diagnostic tests are often available for a disease.
A standardization of the different multiple diagnostic tools as well as its investigated biorepository. .
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