Re: st: Meta-analysis

[Marcello Pagano <pagano@hsph.harvard.edu>]

Welcome to one of the few areas in statistics where people promote 
throwing data away with impunity.  One could say that whether you 
discard experiments where there are no events on either arm, or not, 
depends which side of the argument you are on.  Since no events on 
either arm is evidence towards equality of the two arms, then throw the 
data away if you are trying to show a difference :-)    Otherwise, why 
throw the data away?

The usual reason proffered for discarding the data is that it is 
embarrassing when looking at odds ratios to have to divide zero by zero. 
But the question you have to ask yourself is why are you looking at odds 
ratios?  If you have to, for example if you have a case-control study, 
then you have a problem, otherwise stay clear of odds ratios and you 
won't have a problem if you use appropriate methods for analysis.

m.p.

On 3/17/2008 1:24 PM, Tom Trikalinos wrote:

> this is a big topic.
> see for example:
>
> Stat Med. 2007 Jan 15;26(1):53-77. Much ado about nothing: a
> comparison of the performance of meta-analytical methods with rare
> events.   Bradburn MJ, Deeks JJ, Berlin JA, Russell Localio A.
>
> Stat Med. 2004 May 15;23(9):1351-75. What to add to nothing? Use and
> avoidance of continuity corrections in meta-analysis of sparse data.
> Sweeting MJ, Sutton AJ, Lambert PC.
>
> and quite a few other papers that are out there.  The ones discussing
> the recent rosiglitazone meta-analysis are also relevant. Do a PubMed
> search if you have not already.
>
> My take: Assuming you do not go Bayesian and that you use the typical
> garden variety meta-analysis methods:
>
> 1. Random effects per Der Simonian and Laird are probably a no for
> main analyses (biased tau^2 in simulation studies).
> 2. Peto OR seems to do well in terms of bias and coverage
> probabilities for the CI (!).
> 3. Mantel-Haenszel (MH) OR seems to do well, I presume the same for RR
> though i think this is not as clear, or so i remember.
> 4. M-H RD is reported to give somehow biased estimates and conservative CI
>
> 5. If you use multiplicative effect sizes - e.g. an OR I would
> calculate main analyses without 0% vs 0% studies, then add them in in
> a sensitivity analysis
>
> All the above allowing for the caveat that one an operational
> knowledge of the relevant methods literature and knowledge of which
> methods need fudge factors to correct for 0 cells...
>
> hope this helps
>
> tom
>
>
>
> On Mon, Mar 17, 2008 at 12:42 PM, Sripal Kumar <sripalkumar@gmail.com> wrote:
>   
> > I was wondering what are your thoughts on meta analysis of trials with
> >  limited number of events.  Should studies with no events be censored
> >  from the analysis?
> >
> >  Any input is highly appreciated.
> >  thanks,
> >  Sripal.
> >     
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