9 months ago by
Guy's Hospital, London
You should only adjust for age and tumour grade in your survival models if you believe that they are important factors to whatever your hypotheses may be. To quote the authors:
We were interested in the effect a gene has on prognosis independent
of factors such as tumor grade and age of a patient.
So, they adjusted for age and tumour grade specifically because they were the focus of their study, i.e., they obviously had the belief that age and tumour grade would confound the effect that a gene's expression has on prognosis, which makes sense. They also appear to have included gender in each model, which is not relevant for all cancers, of course, even though, for breast cancer, there are some male breast cancer patients in the TCGA BRCA cohort.
From what I gather, they built an independent Cox proportional hazards model for each gene, and in each case they included age, gender, and tumour grade, but the included covariates varied for different cancers. They then obtained the p-values for each gene and clustered samples using the top 100 genes (Figure 1). The survival curves that appear in Figure 1 are actually just based on the clusters that they identify in this clustering. From the Cox models, they also obtained the Beta coefficients and did further work with these.
To help you, Cox proportional hazards is implemented in R via the
coxph() function. I have put some code for doing this already on some Biostars posts: