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


1 Answer 1


Just supply the beta coefficients and corresponding standard errors to the rma() function. So, your syntax should be like this:

rma(coef, sei=se, data=dat)

where coef is the name of the variable in dataset dat denoting the coefficients and se is the name of the variable for the corresponding standard errors. The standard errors already include the information about the number of samples (and actually, it's the number of "events", not the sample sizes, that determine the size of the standard errors).

  • 1
    $\begingroup$ Yes, this is what I have been doing. I am just worried that it might not be rigorously correct. Can you expand on what you mean by your last sentence? Let's say I have 50 samples, that's 50 measurements and 50 survival times from the corresponding patient. When might the number of events differ from the number of samples (except the trivial case where there is missing data)? $\endgroup$ Commented Oct 4, 2015 at 21:57
  • $\begingroup$ When survival times are censored. Maybe the event is 'death' and not everybody in the sample has died (yet). But maybe this does not apply in your case and the event of interest has occurred for everybody. At any rate, the SE of the coefficient is all you need. It includes all the information about sample size and the number of events. $\endgroup$
    – Wolfgang
    Commented Oct 5, 2015 at 5:34
  • $\begingroup$ Ok, yes that's exactly the type of data I have. Thanks so much. $\endgroup$ Commented Oct 5, 2015 at 12:38

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