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Re: st: providing raw weights for multivariate meta-analysis

From   "Seed, Paul" <>
To   "" <>
Subject   Re: st: providing raw weights for multivariate meta-analysis
Date   Mon, 11 Jul 2011 17:40:01 +0100

Nick Darson might benefit from looking at the user-defined commands that Stata 
has for meta-analysis - in particular -metan-, -metareg- and -mvmeta-.
The last is designed to address his exact problem directly.
findit meta will give a more comprehensive list.

Going back to his original question, there are 3 standard systems of weights 
in Stata, none of which appear to be what he wants:
[fweight=f] is appropriate for frequency data - clearly not what he has.
[aweight=n] assume that the values given are means (or averages), from samples of size n; 
all drawn from populations with the same SD. This is not what he has
[pweight=w] are for weighted samples, and include a Huber-White robust SE correction; 
so that only the relative sizes of the weights are important.  

gllamm and xtmixed are both highly flexible commands (gllamm more so, but also 
substantially slower to run).


Paul T Seed, Senior Lecturer in Medical Statistics, 
Division of Women's Health, King's College London and King's Health Partners
020 7188 3642.

On Sun, Jul 10, 2011 at 2:54 PM, Nick Darson <> wrote:
> Thanks a lot for the info and the links.
> I have not looked much into gllamm yet, but the description of Stata
> 12 xtmixed looks very promising for the weighting issue (I guess not
> being able to provide specific weights for each level was the problem
> to which Hox 2010 referred).
> With regard to the "non-normalized" weighting issue, I found in the
> mean-time the following link, which I wanted to share:
> The site states that "pweight does not automatically normalize the
> sample weight like aweight does.  Stata's survey commands do not allow
> aweight.
> Note:  A normalized sample weight sums to the number of observations
> in the data set and its mean is 1. "
> Hence, it looks to me as if I can simply use "pweights", providing the
> effect size variances to the first level as weights (and pweights
> provides the inverse weight of this variable, non-normalized). Then,
> restraining the first level variance to 1 and the multivariate model
> should work (I hope).
> I might try estimating such a model in Stata 12 and compare the
> results with those of HLM using the V-known function (eventually, I
> want to use Stata as I would like to add another level - HLM only
> allows three levels when using the V-known function).

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