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.
|Titolo:||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 (Primo) (Corresponding)
|Parole Chiave:||Hazard function; Neural networks; Piecewise exponential model; Survival analysis|
|Settore Scientifico Disciplinare:||Settore MED/01 - Statistica Medica|
|Data di pubblicazione:||lug-2018|
|Digital Object Identifier (DOI):||10.1186/s12859-018-2179-1|
|Appare nelle tipologie:||01 - Articolo su periodico|