[R-meta] Network meta-analysis with randomized and non-randomized studies in metafor

Chris Rose m@iling@li@t@ @ending from y@letown@io
Wed Nov 21 10:24:32 CET 2018


I'm working on a network meta-analysis (NMA) that will include randomized studies (RS) and non-randomized studies (NRS). I anticipate that the NRS may be biased, and that magnitude and direction of bias may vary unpredictably across the NRS. I am less concerned that the NRS may be overly precise (though I may be wrong!). It is also possible that I will have to include fixed effects for moderators (i.e., do a network meta-regression). I'm considering using the metafor package (see the end of this email for my reasoning).

How should I model the distinction between the RS and NRS in metafor? I’m considering using a random effect (intercept) for each NRS. I.e., each effect estimate would be supported by some mixture of direct and indirect evidence that is (hopefully) unbiased in the case of the RS, and potentially biased in the case of the NRS (with that bias explained by the random effects).

How would I specify this model in metafor? I.e., how do I specify that the random effects are conditional on a study being a NRS? Could I use a factor variable that has the same value for all the randomized studies, and distinct values for the non-randomized studies? Is there a better way to do this?

Thanks in advance,


Why metafor?

I’m aware of various NMA packages available for R, and I'm able to implement bespoke Bayesian models in JAGS and Stan. I’m considering metafor because:

1. I need to use R.
2. I have multiple outcomes to analyze and need to do various sensitivity analyses, so run-time is a consideration and MCMC is less attractive for this reason.
3. I'd prefer to use a well-tested model implementation (I'm experienced enough with JAGS and Stan to know it's possible to make programming errors).
4. I think the existing NMA-specific packages do not support modeling the distinction between RS and NRS (but I could be wrong).
5. I think the existing NMA packages either do not support regression, or only support a single covariate or continuous covariates. I think this means they do not allow me to adjust for the randomized/non-randomized designs as well as other covariates if that was necessary. Again, I could be wrong.

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