I have a survival cancer clinical trials dataset from which I have generated Cox models using forward likelihood ratio testing within R. These models are based on 'traditional' cancer variables (eg. age, histology, metastasis etc).
I would like to extend the model using high dimensional data (where we have measured many thousands of genes - FWIW, this is DNA methylation data, which can range from zero to one, rather than gene expression). Several approaches have been suggested for investigating survival using high-dimensional data, but I am not aware of any approaches that fit my requirements, i.e. adding high-dimensional data to a base multi-variate model constructed using previously identified survival correlates.
As a first step, I am testing for bi-modality and reducing dimensionality by selecting the most bi-modal probes for further analysis. These probes would be the most amenable to testing and verification in the lab.
One approach would simply be to carry on with the forward LR testing, although this would leave me very prone to overfitting.
Another (more sensible, in my opinion) approach would be to aggregate collections of genes into (survival-related) metagenes and then trim the metagenes into a handful of testable genes, so that this could be a usable test clinically, although this may also be prone to overfitting.
The cancer I work on is rare and test/training cohorts are tricky. To put things into perspective, the clinical trials dataset is 135 cases, with a further 55 age-matched non-clinical-trials cases, which show no difference in survival to the clinical trials dataset.
So my question is, what sort of approaches should I be considering and is what I have done so far sensible?
Any advice from this rather rambling question is most appreciated.
Thanks for reading!