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Re: st: RE: Question regarding meta-analysis for proportions.


From   Nora Trabulsi <nora.trabulsi@mail.mcgill.ca>
To   "<statalist@hsphsun2.harvard.edu>" <statalist@hsphsun2.harvard.edu>
Subject   Re: st: RE: Question regarding meta-analysis for proportions.
Date   Thu, 28 Jul 2011 19:48:20 +0000

Thanks for your response

Yes, this is with using binomial exact. When I generated the proportions and their standard errors, the results shown in the the stata window shows "binomial exact".
Here is the output:

							-- Binomial Exact --
	Variable		Obs		Mean	Std.	Err.		[95% Conf. Interval]
							
				5		1			0		.4781762           1*

(*)	one-sided,	97.5%	confidence	interval

							-- Binomial Exact --
	Variable		Obs		Mean	Std.	Err.		[95% Conf. Interval]
							
				4		1			0		.3976354           1*



So what do you think?

Nora





On 2011-07-28, at 3:38 PM, Forshee, Richard wrote:

> Have you considered using exact binomial confidence intervals instead of the approximation to the Normal distribution? 
> 
> 
> Richard A. Forshee
> 
> -----Original Message-----
> From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Nora Trabulsi
> Sent: Thursday, July 28, 2011 2:36 PM
> To: statalist@hsphsun2.harvard.edu
> Subject: st: Question regarding meta-analysis for proportions.
> 
> Hi
> 
> I am doing a meta analysis on proportions of patients responding to specific treatment. I generated p(proportions) and  se(standard errors). Then , I used the metan command:
> 
> metan p se, random
> 
> The problem that I have encountered is that two of the studies that are included in the analysis had a response rate of 100%, however, they were small in size, 4 and 5 patients only. So this generated a problem as they had standard errors = zero and they were excluded form the analysis and forest plot.
> 
> I tried to use the inverse weight command before running metan:
> 
> gen cons=1
> vwls p cons, sd(se)
> 
> but it would still address the same problem, that std error theta cannot be negative or zero.
> 
> Any idea how to solve this problem, or is it justifiable to remove those 2 studies from the analysis?
> 
> Thanks
> 
> Nora Trabulsi
> 
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