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From |
Steven Samuels <sjhsamuels@earthlink.net> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: RE: regression assumption question |

Date |
Tue, 10 Mar 2009 15:22:33 -0400 |

I suggest that Moleps get get a copy of

-Steve On Mar 10, 2009, at 1:01 PM, Austin Nichols wrote:

moleps islon: I tend to agree with Tony <Peter.Lachenbruch@oregonstate.edu>, but note that if you believe that X affects the change in Y (dY = Ypost - Ypre) but Y is measured with error pre and post, you may prefer not to regress Ypost on Ypre and X, but rather to regress dY on X. Since Ypre is measured with error, its coef may be biased toward zero and you may be able to reject the null that its coef is one even when that is the true model, and you may also have bias in other coefs when you include Ypre, esp if treatment levels are correlated with true baseline Y levels. mat c=(1,0,0,0\ 0,1,0,0.5\ 0,0,1,0\ 0,0.5,0,1) drawnorm e1 y0 e0 x, n(1000) corr(c) seed(1) clear g ypost=y0+x/2+e1 g ypre=y0+e0 g dy=ypost-ypre reg ypost ypre x, nohe r reg dy x, nohe r But of course if Ypre does not have a coef of one in the true model, you will introduce bias by imposing that constraint in a regression of dY on X. Nearest-neighbor matching (findit nnmatch) on pre-treatment observables is another way forward here... On Mon, Mar 9, 2009 at 3:58 PM, Lachenbruch, Peter <Peter.Lachenbruch@oregonstate.edu> wrote:My preference is to use the preop measure as a covariate. If you usethe change, you are essentially forcing the preop to have acoefficientof 1. Sometimes people use the preop as a covariate for the changescore - this automatically induces a fairly high correlation withpreop- if you're not careful, you can believe it. Tony Peter A. Lachenbruch Department of Public Health Oregon State University Corvallis, OR 97330 Phone: 541-737-3832 FAX: 541-737-4001 -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu[mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of molepsislonSent: Monday, March 09, 2009 11:49 AM To: statalist@hsphsun2.harvard.edu Subject: st: regression assumption question Dear listers, I've got data on 300 patients preop and postop using the VAS scale (ordinal scale). I'm trying to locate factors predicting improvement postop. However there are several questions pertaining to this thatI'm unsure of. 1)Do I violate the assumption of independence? Iassumethere would be some correlation between preop and postop within the patients. 2)Would you recommend using delta (preop-postop) as the dependent variable or postop alone? The analyses so far show some heteroscedasticity-in case i violate the independence assumption- is it possible to do add both vce(robust) and vce(cluster id) ? regards Moleps * * 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/* * 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/

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**References**:**st: regression assumption question***From:*moleps islon <moleps2@gmail.com>

**st: RE: regression assumption question***From:*"Lachenbruch, Peter" <Peter.Lachenbruch@oregonstate.edu>

**Re: st: RE: regression assumption question***From:*Austin Nichols <austinnichols@gmail.com>

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