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[R-meta] Computing treatment effects with 95%CI from a meta-regression output
5 messages · Tobias Saueressig, James Pustejovsky, t@s@ueressig m@iii@g oii gmx@de
The easiest thing to do is probably to simply recalculate the model after omitting the intercept term: robu(formula = es ~ 0 + Time, data = SMD,studynum = study, var.eff.size = var,rho = .8, small = TRUE, modelweights = "CORR") Sent from my iPhone
On Sep 6, 2020, at 7:25 AM, Tobias Saueressig <t.saueressig at gmx.de> wrote: robu(formula = es ~ 1 + Time, data = SMD,studynum = study, var.eff.size = var,rho = .8, small = TRUE, modelweights = "CORR")
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That makes sense. RVE won't work and is not appropriate if you only have a single study falling into a given category. For it to work, there need to be multiple independent studies that contribute effects to a given category. On Sun, Sep 6, 2020 at 10:22 AM Tobias Saueressig <t.saueressig at gmx.de> wrote:
Hello James, thank you. It worked. In cases were there was only a single study it gave alot of NaNs. But I have the CIs from the analysis with the intercept in it. Regards Tobias *Gesendet:* Sonntag, 06. September 2020 um 16:53 Uhr *Von:* "James Pustejovsky" <jepusto at gmail.com> *An:* "Tobias Saueressig" <t.saueressig at gmx.de> *Cc:* "r-sig-meta-analysisr-project.org" < r-sig-meta-analysis at r-project.org> *Betreff:* Re: [R-meta] Computing treatment effects with 95%CI from a meta-regression output The easiest thing to do is probably to simply recalculate the model after omitting the intercept term: robu(formula = es ~ 0 + Time, data = SMD,studynum = study, var.eff.size = var,rho = .8, small = TRUE, modelweights = "CORR") Sent from my iPhone
On Sep 6, 2020, at 7:25 AM, Tobias Saueressig <t.saueressig at gmx.de>
wrote:
robu(formula = es ~ 1 + Time, data = SMD,studynum = study, var.eff.size
= var,rho = .8, small = TRUE, modelweights = "CORR")
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