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From |
jverkuilen <jverkuilen@gc.cuny.edu> |

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

Subject |
RE: st: AIC and BIC to compare parametric and non-parametric survival models |

Date |
Sun, 17 May 2009 22:51:59 -0400 |

If the likelihoods aren't comparable---you would need to check the equations to be sure---no. One way things go off the rails is if the normalization terms are dropped and tw different families are compared. Example: To compare say the gamma and lognormal by AIC, you need all the 2*pi and whatever even if they don't affect the estimates in any way. JV -----Original Message----- From: "Tom Trikalinos" <ttrikalin@gmail.com> To: statalist@hsphsun2.harvard.edu Sent: 5/16/2009 3:05 PM Subject: Re: st: AIC and BIC to compare parametric and non-parametric survival models Maarten thanks very much- precise and clear instructions, as always. Out of curiosity, though: is it theoretically correct to use BIC or AIC to compare fit between Cox and a parametric model (e.g., exponential)? t On Fri, May 15, 2009 at 4:13 PM, Maarten buis <maartenbuis@yahoo.co.uk> wrote: > > --- On Fri, 15/5/09, Tom Trikalinos wrote: >> To compare non-parametric and parametric survival >> analysis models, can I use the AIC and BIC? >> Specifically, I fit Cox PH models and exponential and >> weibull parametric regressions. It was pointed out to >> me that AIC & BIC-based comparisons may not be valid >> (because Cox uses partial likelihood). >> >> PS. I am performing survival analyses to inform a decision >> analysis. For this reason I strongly prefer to fit >> parametric models - will make life easier and restore the >> smile on me face. > > You could try estimating a piecewise constant model. The > idea is very similar to the idea behind -stcox-: estimate > a flexible baseline hazard and the explanatory variable > multiplicatively move this baseline hazard up or down. > Alternatively you could model the baseline hazard with > some other flexible curve, like a restricted cubic > spline. See the example below: > > *---------------- begin example -------------------------- > sysuse cancer, clear > gen long id = _n > stset studytime, failure(died) id(id) > stsplit t, every(1) > gen t3 = floor((t)/3) > > // piecewise constant > xi: streg i.t3 i.drug age, dist(exp) > adjust _Idrug_2=1 _Idrug_3=0 age , by(t3) exp gen(haz_piece) > > // restricted cubic spline > mkspline tsp=t, cubic knots(5 10 20 30 35) > xi: streg tsp* i.drug age, dist(exp) > adjust _Idrug_2=1 _Idrug_3=0 age , by(t) exp gen(haz_cubic) > > twoway line haz* studytim, sort c(J) /// > legend(order(1 "piecewise" "constant" /// > 2 "restricted" "cubic spline")) /// > ytitle(hazard) > *----------------- end example ------------------------ > > Hope this helps, > Maarten > > ----------------------------------------- > Maarten L. Buis > Institut fuer Soziologie > Universitaet Tuebingen > Wilhelmstrasse 36 > 72074 Tuebingen > Germany > > http://home.fsw.vu.nl/m.buis/ > ----------------------------------------- > > > > > > > * > * 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/

**Follow-Ups**:**Re: st: AIC and BIC to compare parametric and non-parametric survival models***From:*Tom Trikalinos <ttrikalin@gmail.com>

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