Modeling the covariates effects on the hazard function by piecewise exponential artificial neural networks: an application to a controlled clinical trial on renal carcinoma

BMC Bioinformatics

Elia Biganzoli¹  Marco Fornili¹  Patrizia Boracchi¹  Federico Ambrogi¹ 

[1] Department of Clinical Sciences and Community Health, University of Milan;

关键词: Hazard function;    Neural networks;    Piecewise exponential model;    Survival analysis;   

DOI  :  10.1186/s12859-018-2179-1

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown