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From |
siyama@who.int |

To |
statalist@hsphsun2.harvard.edu |

Subject |
RE: st: graphs - help |

Date |
Tue, 25 Feb 2003 13:55:12 +0100 |

Hi Edlira, Ok, letīs take your query: What I want to do is that after getting the estimations say for sex, then to graph sex 1 and 2 by _t. But - after outfiling I can see that I get only the estimation for I_sex1 as I_sex2 has been omitted. As a result instead of getting two lines, I get only 1. So in general the baseline is ommitted for each variable and as a result it is also missing in the graph. Instad what I want to show is the difference in hazard between males and females thus showing the two curves.... I am attaching below the response you got from Robert Gutierrez (rgutierrez@stata.com) - if you keep it simple by assuming a simple model with no frailty, then the steps he suggested as: xi: streg i.sex i.marstat i.occup, dist(gompertz) robust hr foreach v of varlist _I* { replace `v' = 0 } predict h_standard, hazard are giving you the baseline hazard "h_standard". Now this is the baseline hazard for all your categorical variables baseline groups (for example, if I_sex1=Males, Ioccupnew_2=1,..etc). Hence, everytime you outfiling, "h_standard" should be included with the relevant variable. For example, stcurve, hazard at1(h_standard) at2(I_sex2=1) outfile(myhazard1, replace) stcurve, hazard at1(h_standard) at2(_Ioccupnew_1=1) at3(_Ioccupnew_3=1) at4(_Ioccupnew_4=1) at5(_Ioccupnew_5=1) at6(_Ioccu pnew_6=1) at7(_Ioccupnew_7=1) outfile(myhazard2, replace) and so forth for the other covariates in your model. I hope that helps. All the best, Amani -----Original Message----- From: Gjoncae@aol.com [mailto:Gjoncae@aol.com] Sent: Monday, 24 February 2003 13:32 To: statalist@hsphsun2.harvard.edu Cc: rgutierrez@stata.com Subject: Re: st: graphs - help Many thanks for your help. It works well now but I have another question. You add the option alpha1 when predictiong the hazard after running the model with heterogeneity- predict h_frailty, hazard alpha1. Does that mean that you assume shared frailty? In my data, I have no reason to believe that there is shared frailty. Should I use alpha1, unconditional or no option at all? Thank you Edlira ********************************************** Eldira <Gjoncae@aol.com> asks: > I am running two Gompertz models: one without considering heterogeneity - > xi: streg i.sex i.marstat i.occup, dist(gompertz) robust hr > and the other when I take heterogeneity into account as follows (e.g.): > xi: streg i.sex i.marstat i.occup, dist(gompertz) frailty(gamma) robust hr > After this, I would like to have a graph showing the 2 baseline hazard > curves when considering the heterogeneity and when not considering it. In frailty models, there is a distinction between the individual hazard (that faced by individuals) and the population hazard (the average hazard over the whole population). A common phenomenon with such models is that the individual hazard may be ever-increasing, yet the population hazard begins to fall past a certain point. This is known as the frailty effect -- after some time only the most robust individuals remain in the population and thus their average hazard is quite small. That being said, in models without frailty the indiviual and population hazards are the same, and thus it is fair to compare the estimated hazard from a standard model to either of the two hazards from the frailty model. Suppose you are comparing individuals hazards, in which case . xi: streg i.sex i.marstat i.occup, dist(gompertz) robust hr . foreach v of varlist _I* { 2. replace `v' = 0 3. } . predict h_standard, hazard . xi: streg i.sex i.marstat i.occup, dist(gompertz) frailty(gamma) robust hr . foreach v of varlist _I* { 2. replace `v' = 0 3. } . predict h_frailty, hazard alpha1 . graph twoway line h_standard h_frailty _t, sort will do the trick. Of course, I relied on the fact that all of Eldira's regressors were indictators generated by -xi-, meaning I could replace their values at will knowing that they would be restored the next time -xi- was called. In general, you would have to copy the values of your regressors to variables with other names, set the original regressors to zero (baseline), predict, then restore the original regressors' values before running the second regression. Finally, if you want to compare population hazards instead, simply omit the -alpha1- option in the second call to -predict-. --Bobby rgutierrez@stata.com * * 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/ * * 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/

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