𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Comparison of the performance of neural network methods and Cox regression for censored survival data

✍ Scribed by Anny Xiang; Pablo Lapuerta; Alex Ryutov; Jonathan Buckley; Stanley Azen

Publisher
Elsevier Science
Year
2000
Tongue
English
Weight
99 KB
Volume
34
Category
Article
ISSN
0167-9473

No coin nor oath required. For personal study only.

✦ Synopsis

Strategies that have been developed to extend NN prediction methods to accommodate right-censored data include methods due to Faraggi-Simon, Liestol-Andersen-Andersen, and a modiΓΏcation of the Buckley-James method. In a Monte Carlo simulation study, we evaluated the performance of all three NN methods with that of Cox regression models which included main e ects and interactions, when interactions exist. Using the EPILOG PLUSJ PROC NEURAL utility, feed-forward back-propagation networks were examined under nine designs representing a variety of experimental conditions which varied (a) the number of inputs and interactions, (b) the degree of censoring, (c) proportional vs. non-proportional hazards, and (d) sample size.

Minimization methods were implemented that e ciently determined optimal parameters. The C-index was used as a measure of performance. For the testing phase of the study, none of the NN methods outperformed Cox regression. Compared to Cox regression, the Faraggi-Simon, Buckley-James, and Liestol-Andersen-Andersen methods performed as well as Cox regression for 7; 5 and 1 of the nine designs, respectively. The e ect on performance of modeling interactions in Cox regression, varying the number of intervals in the Liestol-Andersen-Andersen method, and varying the NN architecture are also presented. The results of our study suggest that NNs can serve as e ective methods for modeling National Institutes of Health Grants: R03 HL55150 and NS RR33899.

πŸ“œ SIMILAR VOLUMES

Feed forward neural networks for the ana
Feed forward neural networks for the analysis of censored survival data: a partial logistic regression approach
✍ Elia Biganzoli; Patrizia Boracchi; Luigi Mariani; Ettore Marubini πŸ“‚ Article πŸ“… 1998 πŸ› John Wiley and Sons 🌐 English βš– 197 KB πŸ‘ 1 views

Flexible modelling in survival analysis can be useful both for exploratory and predictive purposes. Feed forward neural networks were recently considered for flexible non-linear modelling of censored survival data through the generalization of both discrete and continuous time models. We show that b

Performance comparison between the train
Performance comparison between the training method and the numerical method of the orthogonal neural network in function approximation
✍ Chen-San Chen; Ching-Shiow Tseng πŸ“‚ Article πŸ“… 2004 πŸ› John Wiley and Sons 🌐 English βš– 355 KB

The orthogonal neural network is a recently developed neural network based on the properties of orthogonal functions. It can avoid the drawbacks of traditional feedforward neural networks such as initial values of weights, number of processing elements, and slow convergence speed. Nevertheless, it n

Comparison of neural network and McNish
Comparison of neural network and McNish and Lincoln methods for the prediction of the smoothed sunspot index
✍ FranΓ§oise Fessant; Catherine Pierret; Pierre Lantos πŸ“‚ Article πŸ“… 1996 πŸ› Springer 🌐 English βš– 585 KB

In this paper we propose a comparison between two methods for the problem of hmg-term prediction of the smoothed sunspot index. These two methods are first thc classical method of McNish and Lincoln (as improved by Stewart and Ostrow), and second a neural network method. The results of these two met