st: RE: RE: psmatch2-identifying matched pairs

["Garth Rauscher" <garthr@uic.edu>]

Thanks to Jeanne for the suggestion-that does help to identify the matched
pairs, but it doesn't account for loss in the number of observations that
are identified as part of a matched pair when the ATE option is invoked.

Garth Rauscher
Associate Professor of Epidemiology
Division of Epid/Bios (M/C 923)
UIC School of Public Health
1603 West Taylor Street
Chicago, IL 60612
ph: (312)413-4317
fx:  (312)996-0064
em: garthr@uic.edu
 

-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Jeanne Sears
Sent: Sunday, April 04, 2010 3:08 PM
To: statalist@hsphsun2.harvard.edu
Subject: st: RE: psmatch2-identifying matched pairs

...
Since _weight holds the frequency a particular observation was used as a 
match (and is missing if the observation was not a control or used as a 
match), this may do the subsetting part of what you want:

drop if _weight==.

-- 
Jeanne Sears, PhD, MS, RN
Research Scientist
Department of Health Services &
Center for Healthcare Improvement for Addictions, Mental Illness
and Medically Vulnerable Populations (CHAMMP)
University of Washington
Box 354809
Seattle, WA  98195

e-mail: jeannes@u.washington.edu



Date: Sat, 3 Apr 2010 08:03:56 -0700
From: "Ariel Linden, DrPH" <ariel.linden@gmail.com>
Subject: st: RE: psmatch2-identifying matched pairs

Garth

Per your question about ATE and dropped matches: That makes intuitive sense
since the ATE represents the average treatment effect in the population.
Therefore, you would need to have the outcome values of the entire
population, not just the subset of matched controls. As a result, you would
no longer have 1:1 matches.

Ariel

Date: Fri, 2 Apr 2010 13:57:53 -0500
From: "Garth Rauscher" <garthr@uic.edu>
Subject: st: RE: RE: psmatch2-identifying matched pairs

I managed to figure out how to identify and subset my data to include only
the matched pairs created by psmatch2. What was (and still is) confusing me
was that if I included the ate option, some of the matched pairs were
"broken" and a member of the pair was no longer identified as such. I don't
understand why this happened, but, nonetheless for my purposes, leaving off
the ate option allowed the following code to work:

- - ---------------------------------------------------------------------
psmatch2 path2, pscore(p_prodterms) outcome(binarystage) caliper(.001)
noreplace neighbor(1)

gen pair = _id if _treated==0
replace pair = _n1 if _treated==1
bysort pair: egen paircount = count(pair)
drop if paircount !=2
- - ---------------------------------------------------------------------

This code creates the variable pair that identifies the matched pairs.


I forgot to mention- Hat tip to the authors of psmatch2 for providing this
partial solution...

- - ------------------------------------------------------------------------
The following code:

gen pair = _id if _treated==0
replace pair = _n1 if _treated==1

creates a variable that identifies the matched pairs but also includes those
within the region of support who are not included in a matched pair. I
cannot figure out how to drop those observations that are not part of a
matched pair. Any help would be appreciated.

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