Errors when including covariates in metaSEM

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Picture of user. forscher Joined: 03/05/2013
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I have been attempting to add covariates to a network meta-analytic model that I'm fitting in metaSEM. As a brief bit of background, the network meta-analytic model is designed to model comparisons between a reference group and a set of other groups. Each comparison between the reference and other groups is modeled as a separate outcome. The S matrix in these models is often quite sparse because it is often the case that only ~half the studies contain multi-group designs, so one often needs to place constraints (e.g., with RE.constraints in metaSEM) on the between-studies covariance matrix for the model to be identifiable.

I am able to fit the model with a covariate using the mvmeta package in R, and I have verified that there is variance in my covariate for each of the outcomes (i.e., comparisons between reference and other groups) in the meta-analysis. However, when I attempt to fit the model with the single covariate in metaSEM, I receive the following error:

Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
0 (non-NA) cases

Does anybody have any idea what's happening here? For reference, I have tried fitting the covariate model on subsets of the data (i.e., using only one of the 11 outcomes from the meta-analysis) without generating these errors. My data and a script are attached.

Replied on Wed, 03/11/2015 - 21:56
Picture of user. Mike Cheung Joined: 10/08/2009

Hi Patrick,

The meta() function uses the regression coefficients of a regression analysis as starting values. In your case, there is no enough data for the regression analysis after excluding the missing data with listwise deletion. You may skip this step by manually providing starting values to meta(). Please see the attached analysis.

Cheers,
Mike

Replied on Thu, 03/12/2015 - 10:53
Picture of user. Mike Cheung Joined: 10/08/2009

In reply to by forscher

No. The so-called "explained variances" are based on comparing the heterogeneity variances with and without the predictors. Since there are constraints on your variance components, I don't think that the meta() function is smart enough to calculate the correct "explained variances."