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From |
"Ariel Linden, DrPH" <ariel.linden@gmail.com> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
Re: st: deriving a bootstrap estimate of a difference between two weighted regressions |

Date |
Wed, 4 Aug 2010 10:07:59 -0700 |

Thank you, Stas. I will take your suggestions under advisement! Date: Tue, 3 Aug 2010 14:37:51 -0500 From: Stas Kolenikov <skolenik@gmail.com> Subject: Re: st: deriving a bootstrap estimate of a difference between two weighted regressions On Tue, Aug 3, 2010 at 1:20 PM, Ariel Linden, DrPH <ariel.linden@gmail.com> wrote: > Thank you Stas and Steve for your comments! > > When I stated that the first model's weight would be ATT and the next > ATC, it was already after running the propensity score model and > establishing the > weights for each subject: > ATT = cond(treatvar, 1, propvar/(1- propvar)), and ATC = > cond(treatvar, (1-propvar)/propvar, 1) > > Under these conditions, there should be no negative weights, so that > is not > a concern. The negative weights would come out of Steve's suggestion to entertain the difference in weights (as that's what your procedure boils down to). > I am thinking that the code would look something like this, but I > would appreciate your input: > > 1. bootstrap _b[treatvar] from first regression with [pw=ATT] 2. save > 10,000 samples to file (or tempfile) 3. bootstrap _b[treatvar] from > second regression with [pw=ATC] 4. save 10,000 samples to file (or > tempfile) 5. gen difference = treatvar1-treatvar2 6. bootstrap > r(mean): sum difference, to get bootstrapped CIs > > Does this make sense? 1-2 will produce something very similar to the _se[treatvar] in your basic regression with ATT weights (probably with -robust- option), and 3-4 will produce something very similar to _se[treatvar] in the regression with ATC weights. I outlined the code for you in the previous message -- you need to bootstrap the whole estimation procedure = { the propensity regression (leading to the weights) + two main regressions with two sets of weights }. In other words, for each bootstrap sample, you would need to run everything in the curly brackets to produce your "difference" estimate. I cannot comment on the scientific validity of this procedure; other people more knowledgeable in treatment effect estimation could do that. * * 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/

**Follow-Ups**:**Re: st: deriving a bootstrap estimate of a difference between two weighted regressions***From:*Steve Samuels <sjsamuels@gmail.com>

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