Thanks Carla, that seems to have some potential to it!
From: Warneke,Carla L [mailto:cwarneke@mdanderson.org]
Sent: Wednesday, 10 October 2018 9:44 AM
To: Emina Omerovic <emina.omerovic(a)mcri.edu.au>au>; stefano iacus
<stefano.iacus(a)unimi.it>
Cc: king-assist(a)iq.harvard.edu; cem(a)lists.gking.harvard.edu
Subject: RE: [cem] Matching for a continuous variable
Hi Emina,
I don’t know the answer to your question for cem, but are you wanting to do caliper
matching?
The code below selects 3 matches with caliper matching on age (matched within 15 years)
and exact matching on stage.
calipmatch, generate(calmatch) casevar(group) maxmatches(3) calipermatch(agei)
caliperwidth(15) exactmatch (stage)
Maybe the two methods can be combined?
Carla
From:
cem-bounces@lists.gking.harvard.edu<mailto:cem-bounces@lists.gking.harvard.edu>
[mailto:cem-bounces@lists.gking.harvard.edu] On Behalf Of Emina Omerovic
Sent: Tuesday, October 09, 2018 5:30 PM
To: stefano iacus <stefano.iacus@unimi.it<mailto:stefano.iacus@unimi.it>>
Cc: king-assist@iq.harvard.edu<mailto:king-assist@iq.harvard.edu>;
cem@lists.gking.harvard.edu<mailto:cem@lists.gking.harvard.edu>
Subject: Re: [cem] Matching for a continuous variable
Hi Stefano,
So if I have a variable of continuous scores and want to match the sample based on scores
within 1 sd (e.g. 15) rather than applying the cutpoints which creates categories of
scores such that
cem gender kbitssw5 (70 85 100 115 130), treatment (group)
becomes the equivalent of
recode kbitssw5 (0/70 = 1 "Well below average") (70/85 = 2 "Below
average") (85/115 = 2 "Average") (115/130 = 3 "Above average")
(130/200 = 3 "Well above average"), gen(kbittssw5_5cat)
cem gender kbittssw5_5cat, treatment(group)
Can I match based on freely changing cutpoints to maintain that 1 sd ? Eg. A participant
with a score of 80 could be matched to someone with a score anywhere between 65 and 95,
and not just between 70 and 85
Kind regards
Emina
From: stefano iacus [mailto:stefano.iacus@unimi.it]
Sent: Tuesday, 2 October 2018 1:55 PM
To: Emina Omerovic
<emina.omerovic@mcri.edu.au<mailto:emina.omerovic@mcri.edu.au>>
Cc: cem@lists.gking.harvard.edu<mailto:cem@lists.gking.harvard.edu>;
king-assist@iq.harvard.edu<mailto:king-assist@iq.harvard.edu>
Subject: Re: [cem] Matching for a continuous variable
Hi,
if I understand correctly, you just need to specify the cutpoints argument in the cem()
function like on page 10 (bottom) of the pdf document you mention.
Usually the best thing to do is to write a simple reproducible example (R code + sample of
data) so that we can help you to solve your problem.
Best regards
Stefano
On 26 Sep 2018, at 04:11, Emina Omerovic
<emina.omerovic@mcri.edu.au<mailto:emina.omerovic@mcri.edu.au>> wrote:
From:
cem-bounces@lists.gking.harvard.edu<mailto:cem-bounces@lists.gking.harvard.edu>
[mailto:cem-bounces@lists.gking.harvard.edu] On Behalf Of Emina Omerovic
Sent: Wednesday, 26 September 2018 11:38 AM
To: cem@lists.gking.harvard.edu<mailto:cem@lists.gking.harvard.edu>
Subject: [cem] Matching for a continuous variable
Hello,
Using the example provided
http://scholar.harvard.edu/files/gking/files/cem.pdf<http://scholar.harv…
I am not sure how to approach matching for continuous scores eg. An IQ score. Using my
data sample and the automated method, it appears all matches are within about 8 score
points. If we wanted to define the coarsening to about within 15 points (eg within 1 SD)
how could I do this?
Thank you
Looking forward to the reply
Emina Omerovic BSc, MClAud
Research Assistant
Intergenerational Health
Murdoch Children's Research Institute
The Royal Children's Hospital, 50 Flemington Road
Parkville, Victoria 3052 Australia
E emina.omerovic@mcri.edu.au<mailto:emina.omerovic@mcri.edu.au>
W mcri.edu.au<https://www.mcri.edu.au/>
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