.
There is at that URL, optmatch2.ado and optmatch2.hlp.
You can save these files where Stata wants them, and then they will be
available to you at the Stata command line. There won't be any menus.
You might also track don't the author. He has offered email assistance
using this program.
-Dave
----------------------------------------------------------------------------------------
help for optmatch2
----------------------------------------------------------------------------------------
Optimal Matching
Syntax
optmatch2 casecontrol varlist [if] [in] [, options ]
options description
----------------------------------------------------------------------------------
Main
minc(#) Minimum control:case ratio.
maxc(#) Maximum number of controls per set.
nc(#) Total number of controls to include in
match.
gen(newvar) A new variable to contain the number of
the case-control
set each subject belongs to.
caliper(#) Limit on acceptable matching.
measure(string) Type of dissimilarity measure to use.
epsilon(#) Stability constant.
repeat If requested number of controls cannot be
matched, produce
match with as many controls as possible.
----------------------------------------------------------------------------------
Description
The command optmatch2 performs optimal matching using the network
flow methodology
outlined in Rosenbaum(1989). The variable casecontrol contains 1
for cases and 0
for controls. The variable(s) on which matching is to be
performed are given by
varlist. If there is more than one variable in varlist, there are
a number of ways
of calculating a distance between a case and a control: see the
option measure
below for more information.
Options +------+
----+ Main
+---------------------------------------------------------------
minc(#) Minimum control:case ratio. May be less than 1: e.g. 0.5
means the same
control can be mapped to 2 cases. Default value is 1.
maxc(#) Maximum number of controls per case-control set. Must be
an integer >= 1:
default value is 1
nc(#) Total number of controls to be used in the match. Defaults
to the all
controls in the dataset. Can be set to any integer less than
or equal to this:
requesting more controls than exist in the dataset will cause
optmatch2 to
fail with an error message.
gen(newvar) If given, this will create a new variable containing
an identifier for
the case-control set this individual belongs to. If it is not
given, a
variable called set is created, unless it already exists in
which case
optmatch2 will fail with an error message.
caliper(#) This sets the maximum allowable discrepancy between a
case and a
control within a matched set. By default, no caliper is set
and every control
can, in theory, be matched to any case.
measure(string) This is only of importance if there are more than
one variable in
varlist. In this case, it determines the metric to use when
converting
differences in several variables to one overall dissimilarity
measure. The
standard measures that stata can use are outline in
measure_option. Of these,
optmatch2 can use L(#), Lpower(#) and Linfinity and their
various aliases,
with the default being L2. In addition, it can accept a value
mahal to use the
Mahalonobis distance.
epsilon(#) Default value is 0.000001. Technically, the optimal
matching method
only works if all discrepancies between cases and controls
are greater than
zero. This value is added to all discrepancies to ensure that
this is the
case. The value of epsilon can affect the matching if (opt
minc} < 1: see
Hansen and Klopfer (2006) for a discussion of this.
repeat It may be impossible for optmatch2 to find a matching that
matches the
requested number of controls (nc). This may be a logical
impossibility (there
are not that many controls in the data) or an empirical one
(if you use
caliper to define the maximum allowable discrepancy in a
match, it may not be
possible to match all controls to a case). If you give the
repeat option, it
will report how many controls it can match, then perform the
matching with
that number of controls. Otherwise, it will simply report the
maximum number
of controls it could match.
Remarks
The command optmatch2 produces matched sets, that is groups
consisting of one or
more cases and one or more controls, with the dissimilarities
between subjects in
a set being as small as possible. By default, it produces matched
pairs (1 case
and 1 control), but this can be changed using the options minc,
maxc and nc. For
example, minc(1) maxc(1) will produce the default 1 to 1
matching, whilst minc(3)
maxc(3) will produce sets which all consist of 1 case and 3
controls.
More complex matchings can be achieved by using values of minc
less than 1. For
example, minc(0.5) maxc(2) will produce sets consisting of either
1 control and 2
cases, 1 control and 1 case or 2 controls and 1 case.
References
Ben B. Hansen and Stephanie Olsen Klopfer: "Optimal Full
Matching and Related
Designs via Network Flows" (2006) Journal of Computational
and Graphical
Statistics 15(3): 609-627.
Paul R. Rosenbaum "Optimal Matching for Observational
Studies" (1989) JASA
84(408): 1024-1302.
Author
Mark Lunt, ARC Epidemiology Unit
The University of Manchester
Please email [email protected] if you encounter problems
with this
program
On Mar 15, 2009, at 11:52 AM, Ishay Barat wrote:
Dear Kieran and David
There can be lots of arguments why one designs backwards study and
not forward one. In my case, I am responsible for a lot of patients,
going through my department, and need form time to time to have
quality control of our patients management. It would have been nice
to have a quarter of a million $ and 3 years time to carry on a
study, but that's not reality.
Sorry.
And now for your question.
As my objective is geriatric patients, and my data includes general
inter medicine ward cliental I like to reduce the noise younger and
far healthier patients introduces into my data.
By matching some crucial parameters like age, sex, medication and
disease, I may get answers to my questions.
As to my anagram. It is just for fun and nothing else.
As to the reference to http://personalpages.manchester.ac.uk/staff/mark.lunt/optmatch.html
I installed the files, but can not find the command in the menus.
*¸..· ´¨)) -:¦:- *
¸.·´ .
(( -:¦:- * Ishay * -:¦:-
´·.. ..·´
((¸¸.·´* -:¦:-
_________________________________________________________-
Matching is an element of the design of a study, planned before the
data is collected, and should be done for efficiency, not control.
If you already have the data, you gain nothing by matching. You
have a sample size of 2,500. If you match these data in the way
you have indicated, you will end up with a matched sample size of
1,200. Why would you want to discard over half of your data?
You should analyse the data as they are and control for age, sex,
etc in the analysis.
______________________________________________
Kieran McCaul MPH PhD
WA Centre for Health & Ageing (M573)
University of Western Australia
Level 6, Ainslie House
48 Murray St
Perth 6000
Phone: (08) 9224-2140
Fax: (08) 9224 8009
email: [email protected]
http://myprofile.cos.com/mccaul
http://www.researcherid.com/rid/B-8751-2008
______________________________________________
Epidemiology is so beautiful and provides such an important
perspective on human life and death,
but an incredible amount of rubbish is published. Richard Peto
(2007)
-----Original Message-----
From: [email protected] [mailto:[email protected]
] On Behalf Of Ishay Barat
Sent: Sunday, 15 March 2009 1:28 AM
To: [email protected]
Subject: st: CASE-CONTROL STUDY
HELLO
I've got a data set containing about 2500 patients, of which 300
have my
interest (Group A).
I would like to extract a sample of 900 patients (Group B) out of
the data
set that match Group A in age, sex and some other parameters. A
Classical
Case-Control study with 3 controllers for each case.
Is anybody have a clue how the syntax look like??
*¸..· ´¨)) -:¦:- *
¸.·´ .
(( -:¦:- * Ishay * -:¦:-
´·.. ..·´
((¸¸.·´* -:¦:-
*
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