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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   Sat, 30 Jul 2011 15:28:35 +0000

Thanks Austin
The problem in my case is that I cannot use OR as there are no "unexposed" group. It is a meta analysis of phase 2 trials, in which all patients receive the intervention of interest and then the response(yes/no) rates are calculated, and that's why I thought of choosing proportions as the effect estimate. 

I have no experience with bayesian analysis in stata, however your approach sounds interesting and challenging! I must read about Bayesian in stata and give it a try and let you know. 

Thanks again

Nora

Sent from my iPhone

On 2011-07-30, at 9:34 AM, "Austin Nichols" <austinnichols@gmail.com> wrote:

> Nora Trabulsi <nora.trabulsi@mail.mcgill.ca> :
> If you are working on a log odds scale as you should for meta-analysis
> of proportions, you will have problems with the point estimate, not
> just the standard error.  One way forward would be to use the mean and
> variance of the posterior distribution in a Bayesian framework, with a
> uniform prior in each study.  Probably true Bayesians would object to
> this miscegenation of Bayesian and frequentist approaches, but I am
> betting that if you simulate the approach, it dominates others in
> terms of MSE.  It does not seem justifiable to remove the 2 studies
> with the highest outcome from the analysis since you will introduce
> bias by selecting on the outcome.
> 
> On Thu, Jul 28, 2011 at 3:48 PM, Nora Trabulsi
> <nora.trabulsi@mail.mcgill.ca> wrote:
>> 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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