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From |
Scott Baldwin <baldwinlist@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

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 -- http://www.stata.com/stata12/multilevel-models-with-survey-data/. 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. Best, Scott On Sun, Jul 10, 2011 at 2:54 PM, Nick Darson <nick.darson@googlemail.com> 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: > > http://www.cpc.unc.edu/projects/nutrans/research/usda/help/home > > 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 <austinnichols@gmail.com> wrote: >> Nick Darson <nick.darson@googlemail.com>: >> Have you checked out -gllamm- yet (on SSC and at http://www.gllamm.org/)? >> Official commands in Stata 11 does not allow different weights at >> different levels, but -xtmixed- in Stata 12 will: >> http://stata.com/stata12/multilevel-models-with-survey-data/ >> 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 >> http://www.stata.com/statalist/archive/2005-10/msg00079.html >> http://www.stata.com/statalist/archive/2007-11/msg00812.html >> http://www.stata.com/statalist/archive/2009-05/msg01011.html >> and look up Sean Reardon's -hlm- package on SSC. >> >> >> On Sun, Jul 10, 2011 at 1:43 PM, Nick Darson <nick.darson@googlemail.com> 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 <skolenik@gmail.com> 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 <nick.darson@googlemail.com> 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). >> >> * >> * For searches and help try: >> * http://www.stata.com/help.cgi?search >> * http://www.stata.com/support/statalist/faq >> * http://www.ats.ucla.edu/stat/stata/ >> > > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ > * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: providing raw weights for multivariate meta-analysis***From:*Nick Darson <nick.darson@googlemail.com>

**Re: st: providing raw weights for multivariate meta-analysis***From:*Stas Kolenikov <skolenik@gmail.com>

**Re: st: providing raw weights for multivariate meta-analysis***From:*Nick Darson <nick.darson@googlemail.com>

**Re: st: providing raw weights for multivariate meta-analysis***From:*Austin Nichols <austinnichols@gmail.com>

**Re: st: providing raw weights for multivariate meta-analysis***From:*Nick Darson <nick.darson@googlemail.com>

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