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

From   Scott Baldwin <>
Subject   Re: st: providing raw weights for multivariate meta-analysis
Date   Sun, 10 Jul 2011 17:52:37 -0600

I don't believe you can arbitrarily constrain residual variances in
xtmixed. Weighting in xtmixed will receive more support in Stata 12 --
gllamm should be able to do what you need to do though.

That being said, if by multivariate meta-analysis you mean a
meta-analysis with two or more outcomes per study, you can try the
user-written command --mvmeta--. I've found it reasonably easy to use.
Type --findit mvmeta-- in Stata. There's even a Stata Journal article
about it.


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).
> On Mon, Jul 11, 2011 at 4:02 AM, Austin Nichols <> wrote:
>> Nick Darson <>:
>> Have you checked out -gllamm- yet (on SSC and at
>> Official commands in Stata 11 does not allow different weights at
>> different levels, but -xtmixed- in Stata 12 will:
>> My own inclination is not to impose normality using RE/xtmixed but to
>> run a fixed effects model instead, as it requires weaker assumptions.
>> You can absorb one dimension of fixed effects with -areg- if you don't
>> want to include a large number of dummies.
>> In that case, you can multiply your precision weights times any other
>> weights, rescale, and specify the product as pweights.
>> The point estimates should be identical whether specifying pweights or
>> aweights; only SEs differ (pw=aw+_robust).
>> See also
>> and look up Sean Reardon's -hlm- package on SSC.
>> On Sun, Jul 10, 2011 at 1:43 PM, Nick Darson <> wrote:
>>> Thanks for your help, Stas.
>>> The book I referred to is: Joop J. Hox 2010: Multilevel analysis:
>>> techniques and applications, 2nd edition, Routledge
>>> To my understanding, "aweights" normalizes the weights (such that the
>>> sum of the weights =N, the number of observations).
>>> However, it is important for the multivariate meta-analysis approach
>>> that the weights are not normalized (so called precision weights or
>>> raw weights).
>>> This is one of the two mentioned requirements by Hox (2010). The
>>> second requirement for multivariate meta-analyses is the constraint of
>>> the lowest-level variance to 1 - what is given in Stata - please
>>> correct me if I am wrong on this).
>>> Therefore, I was wondering whether the weight issue is the reason that
>>> Stata cannot be used for multivariate meta-analyses.
>>> Cheers,
>>> Nick
>>> On Mon, Jul 11, 2011 at 3:12 AM, Stas Kolenikov <> wrote:
>>>> I personally think that -help weights- explains it all. Variance
>>>> weights are aweights: they state that the measurement error in a given
>>>> observation is such and such. The pweights are probability weights,
>>>> and when you invoke these, the calculation of not only the point
>>>> estimates, but also the standard errors is different (according to
>>>> probability sampling theory rather than the likelihood theory). There
>>>> are also frequency weights, which is essentially the result of
>>>> -collapse (count) ... , by(*)-. The iweights are "all other weights,
>>>> whichever these may mean for you" -- there is no strict definition,
>>>> and the program that allows them usually has its own idea what to do
>>>> with them.
>>>> Knowing very little about meta-analysis beyond the fancy name, I have
>>>> a feeling that you should be looking towards -gllamm- rather than
>>>> -xtmixed- to find the flexibility with the weights that you need. You
>>>> might still want to run (the much faster) -xtmixed- first to get good
>>>> starting values that you'd feed to -gllamm-. Joop Hox (a proper
>>>> reference is in place, according to the rules of Statalist; I have a
>>>> vague idea that this is a famous multilevel author) is a somewhat
>>>> opinionated guy, to my impression (although I am much more opinionated
>>>> than he is :)).
>>>> On Sun, Jul 10, 2011 at 7:02 AM, Nick Darson <> wrote:
>>>>> Dear Statalisters,
>>>>> I attempt to carry out a multivariate meta-analysis using "xtmixed". I
>>>>> have three outcome measures (the first level).
>>>>> When using a multilevel approach for the multivariate meta-analysis, I
>>>>> read that it is important to provide raw-weights (non-normalized) of
>>>>> the inverse sampling variances for the first level (containing the
>>>>> outcome measures).
>>>>> I know that HLM6 has a special function for this ("V-known") for two
>>>>> and three level models.
>>>>> Now, I wanted to know whether "pweights" is an equivalent solution for
>>>>> xtmixed in Stata?
>>>>> I must admit that I am a bit confused by the various descriptions of
>>>>> the weight functions I found online (aweights, iweights, etc - though
>>>>> this are not allowed for xtmixed) .
>>>>> Moreover, surprisingly,  I read in Hox (2010,p. 230) that public
>>>>> domain software for multilevel analysis does not support the required
>>>>> options for a multivariate model so far (i.e. providing raw weights
>>>>> and being able to constrain the lowest-level variance to 1 - however,
>>>>> both should be feasible).
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