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Re: st: AIC and BIC to compare parametric and non-parametric survival models


From   Tom Trikalinos <ttrikalin@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: AIC and BIC to compare parametric and non-parametric survival models
Date   Mon, 18 May 2009 13:27:12 -0400

Thanks, guys -- this is very helpful.


t

On Sun, May 17, 2009 at 10:51 PM, jverkuilen <jverkuilen@gc.cuny.edu> wrote:
> 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/
>> -----------------------------------------
>>
>>
>>
>>
>>
>>
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