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From |
"Martin Weiss" <martin.weiss1@gmx.de> |

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

Subject |
st: RE: predicting survival with a semiparameteric model |

Date |
Tue, 24 Aug 2010 19:29:25 +0200 |

<> So your problem is pretty much the inverse of http://www.stata.com/statalist/archive/2009-11/msg00132.html ? HTH Martin -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Feiveson, Alan H. (JSC-SK311) Sent: Dienstag, 24. August 2010 19:24 To: statalist@hsphsun2.harvard.edu Subject: st: predicting survival with a semiparameteric model Hi - I am estimating a semi-parametric survival model using -stcox-. In the process, I can get estimates of the cumulated baseline hazard (say H0(t)) evaluated at values of time, t, in my data. Then I can use this to predict the survival at time t for given values of the explanatory variables, say x, using S(t) = exp(-exp(xb)H0(t)) where xb is the linear predictor. However, I don't see how to get a standard error of this prediction. For example, -nlcom- only considers the estimation error in xb and treats H0(t) as a known constant. So is there a way to incorporate the uncertainty in estimating H0(t) into the standard error of S(t)? Of course, I can always get a standard error of S(t) with a fully parametric model using -streg-, but if possible, I'd like to use a PH model without having to specify a distribution. Thanks Al Feiveson * * 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/

**Follow-Ups**:**st: RE: RE: predicting survival with a semiparameteric model***From:*"Feiveson, Alan H. (JSC-SK311)" <alan.h.feiveson@nasa.gov>

**References**:**st: predicting survival with a semiparameteric model***From:*"Feiveson, Alan H. (JSC-SK311)" <alan.h.feiveson@nasa.gov>

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