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From |
"Austin Nichols" <austinnichols@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: model-based standardization |

Date |
Tue, 5 Feb 2008 00:30:21 -0500 |

Garth, I haven't looked at the ref you cite, but what you want is one of the several quantities that economists refer to as marginal effects (see e.g. the description of -margfx- and SE calcs at http://glue.umd.edu/~gelbach/ado/margfx.pdf). Your code fails because you have not multiplied estimated coefficients by variables. Try instead e.g. logit Y X r2 r3 a2 a3 g p0=invlogit(_b[_cons]+ _b[r2]*r2+_b[r3]*r3+_b[a2]*a2+_b[a3]*a3) g p1=invlogit(_b[_cons]+_b[X]+_b[r2]*r2+_b[r3]*r3+_b[a2]*a2+_b[a3]*a3) but it's easier to use predict in any case: ren X wasX g X=0 predict pr0 replace X=1 predict pr1 drop X ren wasX X though you still have to make sure there are no neglected connections between X and other variables (e.g. interactions). To bootstrap, simply wrap it in a program: prog dp cap drop pr0 pr1 dp logit Y X r2 r3 a2 a3 ren X wasX g X=0 predict pr0 replace X=1 predict pr1 drop X ren wasX X g dp=pr1-pr0 mean pr0 pr1 dp end bs: dp On Feb 4, 2008 11:11 PM, Garth Rauscher <garthr@uic.edu> wrote: > [I tried to send this message to the listserv a few days ago but don't think > it made it through so I am trying again. I apologize if this is a duplicate > message.] > > Dear listserve members > > I am attempting to learn how to perform a model-based standardization with > Stata, using the marginal or predictive margins method. I would like to be > able to estimate standardized probabilities and probability differences from > logistic regression that are standardized to the distribution of modeled > covariates. The idea is summarized in: "Greenland S. Model-based estimation > of relative risks and other epidemiologic measures in studies of common > outcomes and in case-control studies. Am J Epidemiol 2004;160:301-305." To > the best of my understanding, the method involves estimating predictied > probabilities of Y under two scenarios (e.g. x=1 and x=0). Assuming we have > a dependent variable Y(0,1), an exposure of interest X(0,1), and covariates > r2 r3 a2 a3 a4 a5, two sets of predicted probabiltiies could be: > > P0(x) based on the joint distribution of covariates, with X=0 assigned to > everyone > P1(x) based on the joint distribution of covariates, with X=1 assigned to > everyone > PD(x) as the difference in probabilities, P1(x) - P0(x) > > Below is my code. > logit Y X r2 r3 a2 a3 a4 a5 > // predicted xbetas after assigning all observations to X=0 > g if0=_b[_cons]+_b[X]*0+_b[r2]+_b[r3]+_b[a2]+_b[a3]+_b[a4]+_b[a5] > // predicted xbetas after assigning all observations to X=1 > g if1=_b[_cons]+_b[X]*1+_b[r2]+_b[r3]+_b[a2]+_b[a3]+_b[a4]+_b[a5] > // predicted probabilities > g p0x = invlogit(if0) > g p1x = invlogit(if1) > I was expecting two new variables of predictied probabilities, p0x and p1x > with a range of values that depended on covariates. However, I noticed that > p0x and p1x each had only one value instead of a range of values as I had > expected (see above). Any clarification as to what I am doing incorrectly > would be appreciated. I think my next task would have been to perform > bootstrapping to get confidence intervals from the distribution of means for > p0x, p1x and PD(x). * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**RE: st: model-based standardization***From:*"Garth Rauscher" <garthr@uic.edu>

**RE: st: model-based standardization***From:*"Garth Rauscher" <garthr@uic.edu>

**References**:**st: model-based standardization***From:*"Garth Rauscher" <garthr@uic.edu>

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