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A Hidden Markov Model approach to analyze longitudinal ternary outcomes when some observed states are possibly misclassified
Understanding the dynamic disease process is vital in early detection, diagnosis, and measuring progression. Continuous-time Markov chain (CTMC) methods have been used to estimate state change intensities but challenges arise when stages are potentially misclassified. We present an analytical likeli...
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| Published in: | Stat Med |
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| Main Authors: | , , , , |
| Format: | Artigo |
| Language: | Inglês |
| Published: |
2016
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| Subjects: | |
| Online Access: | https://ncbi.nlm.nih.gov/pmc/articles/PMC4821697/ https://ncbi.nlm.nih.gov/pubmed/26782946 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1002/sim.6861 |
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