Notice: On April 23, 2014, Statalist moved from an email list to a forum, based at statalist.org.
| From | "Li, Jilan" <jilanli@email.unc.edu> |
| To | "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu> |
| Subject | RE: st: -doseresponse- |
| Date | Fri, 6 Jan 2012 15:12:18 +0000 |
Daniel, Thanks for your comments! Sorry for sending the questions twice to the list. I was not sure if it went through the first time. -doseresponse- uses the GPS and its higher order terms as covariates in the outcome model. Best! JL ________________________________________ From: owner-statalist@hsphsun2.harvard.edu [owner-statalist@hsphsun2.harvard.edu] on behalf of daniel klein [klein.daniel.81@googlemail.com] Sent: Friday, January 06, 2012 9:54 AM To: statalist@hsphsun2.harvard.edu Subject: Re: st: -doseresponse- Julian, -doseresponse- is a user-writen ado file and you are asked to explain where it comes from (http://www.stata.com/support/faqs/res/statalist.html#stata). In this case either from SJ-10-4 or from SSC (I did not check which is the latest version). You are also discuraged to send exactly the same question twice (http://www.stata.com/support/faqs/res/statalist.html#noanswer), which you did (http://www.stata.com/statalist/archive/2012-01/msg00178.html). You migth be better of writing directly to the authors, but that is not guaranteed either, as (at least some of) your questions are beyond the scope of "technical support" and your best option migth therefore be to do some literature research. I cannot say much on the topic, but I would like to comment on your third question. As far as I have seen -doseresponse- implements some kind of propensity score matching (PSM) -- if not stop reading here. As I understand it, the very purpose of using PSM is to make sure we only compare what we have actually observed. If this cannot be done, i.e. if the variables are unbalanced after matching, it cannot be done. Including those variables in a regression framework after matching does not solve the problem of extrapolating (i.e. comparing what we did not observe), which is why we have chosen to use PSM over the regression framework in the first place. So I do not really see how it might be usefull to include unbalanced variables in the outcome model. Best Daniel -- I am using -doseresponse-. I have the following questions: 1. It does not allow for the assessment of common support. Do you have any suggestion on how to exam common support when the treatment is continuous? 2. It only conducts balance check after applying the GPS. How can we know if the balance is improved or not? Or how to check balance before applying GPS accordingly? 3. It is an ideal situation when balance is acheived on all covariates. However, it is often the case that some covarites may remain unbalanced. One strategy is to add the unbalanced covariate into the outcome model together with the GPS. Is it easy to modify the program so it can allow adding unbalanced covarites to the outcome model? The original model only includes T and GPS and their higher-order terms and interactions. Your help is so greatly appreicated! Jilan * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/