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.
| Original language | English |
|---|---|
| Pages (from-to) | 186 |
| Journal | BMC Bioinformatics |
| Volume | 19 |
| Issue number | Suppl 7 |
| DOIs | |
| Publication status | Published - Jul 9 2018 |
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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. / Fornili, Marco; Boracchi, Patrizia; Ambrogi, Federico; Biganzoli, Elia.
In: BMC Bioinformatics, Vol. 19, No. Suppl 7, 09.07.2018, p. 186.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Modeling the covariates effects on the hazard function by piecewise exponential artificial neural networks
T2 - an application to a controlled clinical trial on renal carcinoma
AU - Fornili, Marco
AU - Boracchi, Patrizia
AU - Ambrogi, Federico
AU - Biganzoli, Elia
PY - 2018/7/9
Y1 - 2018/7/9
N2 - 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.
AB - 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.
U2 - 10.1186/s12859-018-2179-1
DO - 10.1186/s12859-018-2179-1
M3 - Article
VL - 19
SP - 186
JO - BMC Bioinformatics
JF - BMC Bioinformatics
SN - 1471-2105
IS - Suppl 7
ER -