Hi everyone,
We have been trying to construct a Lasso-cox model with discrete time. We
conducted follow-up examinations on the epileptic attack after tumor surgical
resection among glioma patients. The patients are followed-up in the 6/12/24
months after surgical resection, which makes the epilepsy-free time discrete
(6/12/24 months). We calcluated many features from the T2-images collected
prior to surgical resection. We aimed to used the image features to conducted a
cox proportional hazards analysis on the epilepsy-free time by R. Hereby, We
have three questions:
1. Is the cox proportional hazards analysis appropriate for our study?
2. The epilepsy-free time is discrete. We suppose that we need to conduct a
cox analysis with discrete time. Is that right?
3. We acquired quite a lot of image features, which makes the feature
selection imperative. We have planned to use the Lasso penalty for feature
selection. We notice that the 'coxnet'/'glmnet'/'penalized' package could be
used to construct the Lasso-cox model. Are they still appropriate for the cox
analysis with discrete time?
Sorry for the poor English expression.
We're still new to R. We're very grateful for any help.
Thank you very much!
Best Regard,
Zhenchao Tang
CAS Key Laboratory of Molecular Imaging, Institute of Automation,Beijing
100190, China
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