I am trying to reproduce this study: http://stm.sciencemag.org/content/7/299/299ra122
I have time-dependent features like patient lab values and vital signs measurements, and also features like age, gender, etc that don't change in time. The paper says "we fit a Cox proportional hazards model using the time until the onset of septic shock as the supervisory signal". And "time-to-event models were learned as a Cox proportional hazards model with lasso regularization (glmnet R package, version 1.9-8"
I am new to survival analysis. I have done a lot of research but have not found one that has used glmnet cox regression with time dependent data. The only example I found is this: https://github.com/cran/glmnet/blob/master/inst/doc/Coxnet.R The data (patient.data) columns are not explained and do not seem to be time dependent.
To feed my data to glmnet package, I know I should have a matrix x (one row for each patient and one column for each feature) and a matrix y (one column for each patient,one row for time of event for each patient and one row for status of event)
My question is, imagine I have 50 patients, and my features are age, gender, type of disease and blood pressure (measured every four hours for a month) and a lab value (measured every day for a month), what should my matrix x look like?