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Re: st: Trend test in meta analysis


From   "Tom Trikalinos" <ttrikalin@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Trend test in meta analysis
Date   Mon, 24 Dec 2007 01:16:02 -0500

To follow up -
a friend alerted me that Nicola Orsini has implemented Greenland's method from

Greenland S. and Longnecker M. P. 1992. Methods for trend estimation
from summarized
        dose-reponse data, with applications to meta-analysis, American Journal
        Epidemiology, 135(11), pp.1301-1309.

_findit glst_


tom


On Dec 22, 2007 2:09 PM, Jayaprakash, Vijay
<Vijay.Jayaprakash@roswellpark.org> wrote:
> Hi Tom, Austin & Ben,
>      Thanks for the reply. The comments and the references mentioned were useful. Being a student trying out meta-analysis by myself, it was really useful information.
>
> Thanks again and a happy holidays to all of you
> Vijay
>
>
>
> ________________________________
>
> From: owner-statalist@hsphsun2.harvard.edu on behalf of Tom Trikalinos
> Sent: Sat 12/22/2007 11:34 AM
> To: statalist@hsphsun2.harvard.edu
> Subject: Re: st: Trend test in meta analysis
>
>
>
>
> Austin,
>
> good points.
> Meta-analysis of observational epi studies is very often performed. I
> do not see it as a fundamental problem as long as the analyst is
> cognizant of the pertinent issues and careful in the interpretation of
> any findings. It is the analysts' duty to interpret the results of a
> synthesis (or a meta-regression or any other exploration) critically -
> quite in the spirit of your comments.
>
> Providing a grand mean is not the sole reason for meta-analysis. In
> fact, in some cases it may even be misleading. Other goals of equal
> importance is to explore and describe between-study heterogeneity, and
> to try to identify traces of systematic errors at the study level and
> at the "scientific field" level.
>
> That being said, meta-analytic techniques may be invaluable tools for
> empirical research. Some have made an enviable career demonstrating
> things many methodologists believed or anticipated on theoretical
> grounds - but noone had actually *shown* before.
>
> As for the technical issue... the papers in the previous e-mails and
> the ones cited in them have dealt with the problem extensively... your
> sign test idea or some modification thereof is definitely interesting
> ;-)
>
> tom
>
>
> On Dec 21, 2007 9:23 PM, Austin Nichols <austinnichols@gmail.com> wrote:
> > Vijay, Tom, and Ben:
> > I have read neither of the references cited by Ben and Tom, but it
> > seems to me a more fundamental problem is that the meta-analysis
> > includes observational studies (I assume). The principle of
> > meta-analysis is to combine several low-precision unbiased estimates
> > to get a high-precision unbiased estimate; if every study exhibits a
> > positive bias, can one even consider a meta-analysis?  In that case,
> > some kind of sensitivity testing  la Rosenbaum's _Observational
> > Studies_ seems in order.
> >
> > That said, I am wondering if some kind of sign test (see -help
> > signtest-) might not be a good way forward, e.g. count the number of
> > studies with s1>0 (where s1 is the coef on category 1, low dose
> > smoker, with the reference category nonsmoker represented by the
> > zero--could also omit comparisons with zero) and s1<0, those with
> > s2>s1 and with s2<s1, those with s2>0 and s2<0, etc. Under the null of
> > no trend, all of these events have probability one half. A similar
> > approach would be to use -nptrend oddsrat, by(category)-, I think.
> >
> > Does that make sense in this context?  This is very far afield from my
> > own field...
> >
> >
> > On Dec 21, 2007 4:59 PM, Tom Trikalinos <ttrikalin@gmail.com> wrote:
> > > Hi vijay
> > >
> > > I'm not clear what form your data have.
> > >
> > > Let me say right off the bat that such a question (dose-response)
> > > could be evaluated on a qualitative basis (with a plot) without giving
> > > a p-value. This should not be discarded as an option, given the strong
> > > assumptions that are inherent in the data-abstraction process during
> > > meta-analysis.
> > >
> > > That being said see if the paper by Jesse Berlin et al.
> > > "Meta-analysis of epidemiologic dose-response data." Epidemiology.
> > > 1993 May;4(3):218-28. PMID: 8512986 helps.
> > >
> > > Two general comments
> > > A.  first summing up the ORs 1 to 10 and then seeing for a trend (e.g.
> > > with a meta-regression) is subject to Simpson's paradox. This is why
> > > you need an approach like the on described in the cited paper.
> > >
> > > B.  I'm not sure how metap could help you.  -metap- does a
> > > meta-analysis of significance levels, a whole family of non-parametric
> > > meta-analysis methods (metap implements 3 of numerous approaches).
> > > This is essentially an omnibus test and therefore does not have the
> > > interpretation you would wish. A meta-analysis of p-values asks is
> > > there evidence of significant deviation from the null in AT LEAST ONE
> > > of the studies?  A typical misinterpretaion of metap results is that
> > > this is the p-value for the overall summary effect.
> > >
> > >
> > > hope these thoughts help
> > >
> > > tom
> > >
> > >
> > >
> > > On Dec 21, 2007 3:52 PM, Jayaprakash, Vijay
> > >
> > > <Vijay.Jayaprakash@roswellpark.org> wrote:
> > > > Hi Stata users,
> > > >        I'm trying to do a meta analysis of data from 10 different studies. The smoking variable in each study is stratified into categories (Category 1:1-10 cigarettes, Category 2: 10-20 cigs, Category3: 20-30 cigs etc.). I calculated the OR for each category (of smoking) by study.  I then did a meta analysis and calculated the OR for each category by random effects model using the meta command:
> > > > meta  or1 lcl1 ucl1, ci eform
> >
> >
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