I'm looking for a bit of guidance on this, as my stats skills are leaving me treating this as a bit more black-boxish than I'm comfortable with.
So I have several datasets where I am employing Cox Regression on survival data and some molecular measurements. I'm using the coxph routine in R from the Survival analysis package. From this, I get a Beta coefficient, standard error, a z-score, p-value, etc.
All of that I'm comfortable with.
What I'd like to do is a meta-analysis over these coefficients from the different datasets. I've been using the Metafor package in R to do this. As input, I've been giving it the beta coefficients and associated standard errors output by the coxph routine.
I'm not entirely sure that the standard errors provided by the coxph are the variances I need to do the meta analysis, or whether I should be using some weighting scheme that take the size of each dataset (number of samples) into effect more explicitly. (e.g. I may have 40 samples in one data set and 700 in another)
thanks for any guidance