Suna
By using the "kitchen sink" approach to estimate the propensity score,
aren't you increasing the underlying error in estimate? That is, the more
variables in the propensity prediction equation, the more error there is in
the propensity estimate itself.
Joe
-----Original Message-----
From: Barlas, Suna [mailto:suna_barlas@merck.com]
Sent: Thursday, April 24, 2003 1:17 PM
To: 's-news@lists.biostat.wustl.edu'
Subject: Re: [S] Propensity score models
It is implemented in two stages. First, you basically fit a logistic (or
probit) regression where dependent variable is probability of being on
treatment and use every independent variable you have in possession. The
problem is prediction and not estimation and therefore you would go with the
"kitchen sink" approach. After you obtain probability of being on treatment
for everybody in the data, you include that as an independent variable in
the second stage model that may model treatment effect on some dependent
variable. One of the most popular ways is to use quintiles to categorize
propensity scores obtained from the first model and include that in the
second model. The idea is similar to matching but in multidimensions. You
try to match people on their propensity scores - this tries to get you
"closer" to randomization.
Best,
Suna.
-----Original Message-----
From: Peter Flom [mailto:flom@ndri.org]
Sent: Tuesday, April 22, 2003 1:50 PM
To: s-news@lists.biostat.wustl.edu
Subject: [S] Propensity score models
I recently attended a talk on Propensity Score Models by Rajeev Dehejia. It
was very interesting. It's a method for dealing with sampling bias in
nonexperimental studies.
I was wondering if anyone had programmed this in S Plus?
Thanks
Peter
Peter L. Flom, PhD
Assistant Director, Statistics and Data Analysis Core
Center for Drug Use and HIV Research
National Development and Research Institutes
71 W. 23rd St
New York, NY 10010
(212) 845-4485 (voice)
(917) 438-0894 (fax)
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