Hi,
I would like to know how to use Stata to combine the effect size such as
control respond rate or active treatment respond rate in a meta analysis and
to generate Confidence Interval for them as well.
Thank you for your kind assistance.
Long
-----Original Message-----
From: [email protected]
[mailto:[email protected]] On Behalf Of Long Nguyen
Sent: Monday, May 14, 2007 4:13 PM
To: [email protected]
Subject: st: Meta regression
Hi,
I'm a new user of STATA and trying to perform a meta regression. I would
appreciate some detail instruction and examples or tutorials that can help
me do meta regression using this new tool. I have the Getting Started..
Manual and installed the metareg module; however, the online help file
offered only limited information.
Thank you for your kind assistance,
Long
-----Original Message-----
From: [email protected]
[mailto:[email protected]] On Behalf Of Brian P. Poi
Sent: Monday, May 14, 2007 11:03 AM
To: [email protected]
Subject: Re: st: Different numbers of observations in sureg
On Mon, 14 May 2007 09:16:36 -0400 Alemu Mekonnen wrote:
> I would like to use different number of observations in the different
> equations when using the seemingly unrelated regression command in stata
> (i.e., sureg). For example, if there are two equations, I wanted to use
> 600 observations for equation 1 and 900 observations for equation 2. My
> question is how do I limit the number of observations to do this. I was
> not able to use the IF command but this applies to all the equations in
> the system.
Alemu,
Allen McDowell illustrates a clever way of fitting the SUR model with
unequal numbers of observations by using the -xtgee- command in a "From
the help desk" article in the Stata Journal (vol. 4, no. 4).
The trick is to recognize that the SUR model
y1 = x1 * b1 + u1
y2 = x2 * b2 + u2
can be "stacked" and written as
[ y1 ] = [ x1 0 ][ b1 ] + [ u1 ]
[ y2 ] [ 0 x2 ][ b2 ] + [ u2 ]
The stacked version looks just like a panel data model with two panels.
One thing to note is in a SUR model, u1 and u2 are allowed to have
different variances, while -xtgee- assumes they have a common variance.
McDowell's article shows how to rescale the data using OLS regression and
how to manipulate your dataset to use with -xtgee-. Then to fit the model
you simply use -xgee- with a Gaussian distribution, identity link
function, and an unstructured correlation matrix.
-- Brian Poi
-- [email protected]
Reference
------------
McDowell, A.W. (2004). From the help desk: Seemingly unrelated regression
with unbalanced equations. Stata Journal, 4(4): 442-448.
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