I wonder if anyone can comment on if the following modelling strategy is valid please? I have a 200 patient survival data set (actually 2 data sets: 40 events and 160 events) and 100,000 ish candidate predictors.
I want to build a prediction model so I plan to use LASSO and elastic net (with glmnet). I was intending to split into 2/3 train, find the best shrinkage lambda (via CV in the training data) and predict on the remaining 1/3 (by c-statistic).
I was planning on say 50 test training splits to see if the same predictors were selected repeatedly and the variability in best lambda and estimated c-statistics in the test dataset.
I was then going to obtain a final model using all the data and some shrinkage parameter (maybe the mean of the best lambdas from the 50 splits - I'd hope they were all similar) I'd then claim the c-statistic of that final model is within the range of the c-statistics found from the 50 train/test splits (maybe around the mean). Are there are flaws in this scheme or room for improvement ??
many thanks in advance