Message-ID: <CAGCrCxZoce8B0GSKNpYb3usez3rXjjc7BGOW2LWUsqu5nJNnmg@mail.gmail.com>
Date: 2019-01-14T15:21:50Z
From: Torsten Hauffe
Subject: Regression when Y has an estimation variance
In-Reply-To: <DM6PR04MB44895AFA6F17F9B0B33DD0A6DB800@DM6PR04MB4489.namprd04.prod.outlook.com>
Bayesian mixed models implemented in MCMCglmm and brms have an argument to
specify the variances for meta analysis. I never used them for this task,
though. However, the author of brms is psychologist where meta analyses are
common.
Cheers!
On Mon, 14 Jan 2019 at 15:52, Dixon, Philip M [STAT] <pdixon at iastate.edu>
wrote:
> Roy,
>
> One relevant literature is that on meta-regression (a generalization of
> meta-analysis). There is a very good handbook by Koricheva, Gurevitch and
> Mengerson. Meta analysis mostly deals with Gaussian responses (or
> transformable to approximately Gaussian). If there has been any work on
> non-gaussian responses, I expect it would be summarized in Koricheva.
>
> The metafor package is one implementation specifically for meta analysis.
> A resource on metafor and other R packages is Schwarzer and Carpenter,
> Meta-analysis with R.
>
> Other programs can also fit the models as mixed models with heterogenerous
> specified variances, lme() and lmer() do not let you do this. lmer()
> doesn't allow heterogeneous variances; lme() does, but only of the form
> k_i*sigma^2 (i.e. variances relative to a common scaling factor, not
> absolutely specified variances). For a meta analysis, you need to specify
> the absolute variance for each estimate. If someone knows how to trick
> lme() to use exactly the error variances that have been specified, I would
> love to hear about it.
>
> Best,
> Philip Dixon
>
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