Modeling the covariates effects on the hazard function by piecewise exponential artificial neural networks : an application to a controlled clinical trial on renal carcinoma
Articolo
Data di Pubblicazione:
2018
Citazione:
Modeling the covariates effects on the hazard function by piecewise exponential artificial neural networks : an application to a controlled clinical trial on renal carcinoma / M. Fornili, P. Boracchi, F. Ambrogi, E. Biganzoli. - In: BMC BIOINFORMATICS. - ISSN 1471-2105. - 19:suppl. 7(2018 Jul), pp. 186.1-186.7. [10.1186/s12859-018-2179-1]
Abstract:
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 of a neural network extension of the piecewise exponential model to study the shape of the hazard function in time in dependence of covariates. The technique is applied to a dataset of 247 renal cell carcinoma patients from a randomized clinical trial.
RESULTS:
An interaction effect of treatment with number of metastatic lymph nodes but not with pathologic T-stage is highlighted.
CONCLUSIONS:
Piecewise Exponential Artificial Neural Networks demonstrate a clinically useful and flexible tool in assessing interaction or time-dependent effects of the prognostic factors on the hazard function.
Tipologia IRIS:
01 - Articolo su periodico
Keywords:
Hazard function; Neural networks; Piecewise exponential model; Survival analysis
Elenco autori:
M. Fornili, P. Boracchi, F. Ambrogi, E. Biganzoli
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