Handling SMDs

[coraschefft]

Hey Gert,

I'm trying to run an analysis with 50 two arm studies and 8 three arm studies using SMDs. I created the SMDs and respective std.errors by running the pairwise() function of the package meta, applying Hedges' g and then preparing the data for mtc.network. For baseline standard errors in three arm trials I entered the mean of all standard errors of pairwise comparisons of the multiarm trial. --> is that correct at all? If not how do I estimate the baseline se?

When running mtc.model() I get the warning In sqrt(diag(sigma)) : NaNs produced. --> how to deal with this warning? Apparently there are negative numbers in the diagonal of a var/cov Matrix.....

[gertvv]

The standard error specified for the reference arm must be smaller than any of the standard errors for the differences specified within the study.

[gertvv]

The standard error of the reference arm squared is the Covariance of each pair of treatment differences, that is Var{A,B} = Var{A} + Var{B} Var{A,C} = Var{A} + Var{C} Quite often if Var_{A} is not known/given, it is imputed by assuming a correlation (usually between 0.3 and 0.7) - you should be able to find examples of this in the literature, e.g. by Salanti.

[coraschefft]

Thank you, just now saw your reply on this account. Best Cora

Von meinem iPhone gesendet

Am 07.06.2020 um 15:57 schrieb Gert van Valkenhoef notifications@github.com:

The standard error of the reference arm squared is the Covariance of each pair of treatment differences, that is Var{A,B} = Var{A} + Var{B} Var{A,C} = Var{A} + Var{C} Quite often if Var_{A} is not known/given, it is imputed by assuming a correlation (usually between 0.3 and 0.7) - you should be able to find examples of this in the literature, e.g. by Salanti.

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