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Modeling the covariates effects on the hazard function by piecewise exponential artificial neural networks: an application to a controlled clinical trial on renal carcinoma
BACKGROUND: In exploring the time course of a disease to support or generate biological hypotheses, the shape of the hazard function provides relevant information. For long follow-ups the shape of hazard function may be complex, with the presence of multiple peaks. In this paper we present the use o...
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| Pubblicato in: | BMC Bioinformatics |
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| Autori principali: | , , , |
| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
BioMed Central
2018
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| Soggetti: | |
| Accesso online: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6069688/ https://ncbi.nlm.nih.gov/pubmed/30066658 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1186/s12859-018-2179-1 |
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