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From | Steven Samuels <sjsamuels@gmail.com> |
To | statalist@hsphsun2.harvard.edu |
Subject | Re: st: RE: Survival analysis - individual survival functions |
Date | Thu, 16 Dec 2010 16:27:18 -0500 |
-You are very welcome, Paul, but I think that using the post-estimation facilities of -stpm2-, as outlined by Paul Lambert, might be better, as well as easier. -stpm2- has the advantage of being parametric, but doesn't require that you pick (and defend!) a parametric distribution.
In any case, with -streg- you'll need -nlcom- to produce estimates and CIs on a suitable scale and then transform back to the probability scale. With -stpm2- it would be simpler to bootstrap, given the ease of producing S(t)/S(t_0).
Steve sjsamuels@gmail.com On Dec 16, 2010, at 3:31 PM, Seed, Paul wrote: Thanks Steve. I had been hoping to avoid doing all the algebra (or at least get some pre-calculated values to check my algebra against). It6 seems I'm out of luck. Date: Wed, 15 Dec 2010 20:09:30 -0500 From: Steven Samuels <sjsamuels@gmail.com> Subject: Re: st: Survival analysis - individual survival functions Paul, -predict- gives values at the observations after every regression command. To get it for arbitrary values of the inputs, you have to do it yourself. S(t| t_0) = S(t)/S(t_0), and the formulas for S(.) are on page 358 of the Stata Manual (V 11). You can get the linear predictor for any values of the X's with -margins- or -adjust- ( Stata<11). Add the appropriate auxiliary parameters (returned results e(theta), e(aux_p), e(gamma), e(sigma), e(kappa)); your t and t_0; then plug everything into the formulas. Steve Steven J. Samuels sjsamuels@gmail.com 18 Cantine's Island Saugerties NY 12477 USA Voice: 845-246-0774 Fax: 206-202-4783 * * 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/